TECHNISCHE UNIVERSITÄT MÜNCHEN

Lehrstuhl für Mikrotechnik und Medizingerätetechnik

A novel glove monitoring system for quantifying neurolog-

ical symptoms during deep-brain stimulation surgery

Houde Dai

Vollständiger Abdruck der von der Fakultät für Maschinenwesen der

Technischen Universität München zur Erlangung des akademischen Grades eines

Doktor-Ingenieurs (Dr.-Ing.)

genehmigten Dissertation.

Vorsitzender: Univ.-Prof. Dr. med. Dr.-Ing. habil. Erich Wintermantel

Prüfer der Dissertation:

1. Univ.-Prof. Dr.rer.nat. Tim C. Lüth

2. Univ.-Prof. Dr.-Ing. Veit St. Senner

Die Dissertation wurde am 22.10.2013 bei der Technischen Universität München eingereicht und durch die Fakultät für Maschinenwesen am 12.05.2014 angenommen.

II

Danksagung

III

Danksagung

I have gained a profound amount of knowledge during my four years of study and research at

the Technische Universität München (TUM). Throughout these years, my research ability has

continually improved.

Here, I first wish to express my sincere gratitude to Prof. Dr. rer. nat. Tim C. Lüth, for his

constant support throughout my graduate studies at the Institute of Micro Technology and

Medical Device Technology (MIMED, TUM). I also wish to express my sincere gratitude to

the Chinese Scholarship Council (CSC) for their financial support of my studies in Germany.

Furthermore, I would like to thank Dr.-Ing. Lorenzo T. D'Angelo, for his patient instruction,

constant encouragement, and friendly assistance in relation to my studies and research. I

would like to thank Jakob Neuhäuser and Yan Zhao for their kind support, insightful research

discussions, and invaluable suggestions for this thesis. I also wish to express my sincere grati-

tude to Jiaxi Shi for his kind suggestions regarding the structure of my dissertation.

I would also like to offer my thanks to Dr. Jan H. Mehrkens, from Ludwig-Maximilians-

Universität München, for his expert suggestions in this study. I would also like to

acknowledge the great amount of help provided in this study by my student Bernward Otten.

In particular, many thanks to Jordan Evans, Jeremiah Hendren, and the other TUM English

Writing Center staff, for their proofreading of this thesis and their invaluable suggestions.

I would like to thank Mrs. Renate Heuser, Barbara Govetto, and Dr. Franz Irlinger for their

kind work.

Also, I offer my thanks to my colleagues Dr. med. Karin Tonn, Dr.-Ing. Khalil Niazmand, Ian

Somlai, Dr.-Ing. Axel Czabke, Cheng Fang, Joachim Kreutzer, and Samuel Reimer.

In addition, I would like to thank Professor Chao Hu, Dr. Wanan Yang, Zhenglong Chen, and

Professor Guotai Jiang, who have always offered their help.

I would like to thank all the kind people at MIMED and TUM.

Finally, to my family, I would like to thank them for their eternal support and encouragement.

Houde Dai,Oct.,2013

IV

Inhalt

V

Index

1. Introduction ...................................................................................................................... 1

1.1 Project Description ...................................................................................................... 1

1.2 Structure of the Thesis ................................................................................................. 2

2. Application Description ................................................................................................... 3

2.1 Clinical Need ............................................................................................................... 3 2.2 Technical Need ............................................................................................................ 7

3. State of the Art .................................................................................................................. 9

3.1 Neurological Symptoms and Assessment Tasks ......................................................... 9 3.2 Intraoperative Assessment of Neurological Symptoms ............................................. 13

3.3 Quantitative Assessment of Neurological Symptoms ............................................... 15

3.4 Commercial Systems for Quantification of Neurological Symptoms ....................... 19 3.5 Research Systems for Quantification of Neurological Symptoms ............................ 21 3.6 Inertial Sensors and Sensor Fusion for Motion Tracking .......................................... 24 3.7 Limitations of Existing Technology .......................................................................... 27

4. Glove Monitoring System .............................................................................................. 30

4.1 Task Description ........................................................................................................ 30

4.2 Expected Advantages ................................................................................................ 33

5. System Concept .............................................................................................................. 35

5.1 Designs with Wireless Communication Interfaces .................................................... 35

5.2 Static System Description .......................................................................................... 36

5.3 Dynamic System Description .................................................................................... 40

6. Prototypical Realization ................................................................................................ 51

6.1 Nine-Axis Direction-Cosine-Method Realization ..................................................... 51

6.2 Prototypes with Wireless Communication Interfaces ............................................... 52 6.3 Materials .................................................................................................................... 56

6.4 Physical Implementation ........................................................................................... 61 6.5 Graphical User Interface Implementation ................................................................. 67

6.6 Calibration of Inertial Sensors and Force Sensor Boxes ........................................... 68 6.7 Combined Version ..................................................................................................... 74 6.8 Conclusion ................................................................................................................. 76

7. Experiments and Discussion .......................................................................................... 78

7.1 Verification of Analytical Methods for Tremor Assessment .................................... 78 7.2 Verification of Hand Grasping Angle Calculation .................................................... 83 7.3 Verification of Analytical Methods for Rigidity Assessment ................................... 86

7.4 Experiment of Tremor Assessment ........................................................................... 98 7.5 Experiment of Bradykinesia Assessment ................................................................ 102 7.6 Conclusion ............................................................................................................... 104

8. Conclusions and Outlook ............................................................................................. 106

8.1 Conclusions ............................................................................................................. 106 8.2 Outlook .................................................................................................................... 107

9. Glossary ......................................................................................................................... 109

Index

VI

10. Bibliography .............................................................................................................. 112

VII

Introduction

1

1. Introduction

1.1 Project Description

Parkinson’s disease (PD) and essential tremor (ET) are the two common movement disorders,

which denote degenerative and progressive disorder of the central nervous system (CNS).

Tremor, bradykinesia, and rigidity are the three primary symptoms of PD (Elbe et al., 2011).

ET is a tremor of the hands when a patient is performing voluntary movements.

Deep-brain stimulation (DBS) is a crucial surgical procedure for PD, ET, and other neurologi-

cal symptoms (Okun et al., 2008). At present, DBS can only relieve the severe symptoms of

PD, ET, and other nervous system disorders, and then improve the quality of patients’ lives.

The precise mechanisms of PD and how DBS works remain uncertain at present (Dauer et al.,

2003). However, the stimulation in specific areas of the patient’s brain by sending high fre-

quency electrical impulses can alleviate symptoms or diminish the side effects of medications.

Therefore, DBS is the most effective surgical procedure for patients with severe symptoms of

PD, ET, dystonia, or dyskinesia when the patients are insensitive to drug medications.

The positioning target area of an electrode is first confirmed by the three-dimensional struc-

ture obtained from magnetic resonance imaging (MRI). During the positioning of the elec-

trode in the brain by using microelectrode-guided mapping, the neurosurgeon may choose an

optimal location based on the results of sensorimotor mapping or subjective methods, involv-

ing observation according to the clinical scales (Unified Parkinson’s Disease Rating Scale,

etc.) and the assessment of handwriting such as a drawing of an Archimedes spiral. This is

usually accompanied by several small movements of the electrode in a target area. Although

used under clinician observation, these largely subjective scales lack validation against the

actual stimulating effect. Furthermore, the coarse resolution of the ratings is insufficient for

assessing small changes in symptom severity. Finally, the extent of inter-clinician and inter-

subject rating variability is unknown (Machado et al., 2003).

There is no designated instrumental method for the accurate monitoring of the stimulating

effect during DBS surgery as of yet. Nevertheless, neurosurgeons need to know the exact po-

sition and stimulating intensity of the deep-stimulation electrode to achieve the best effect.

The goal of the present project is to develop a portable assessment system to quantify the

three primary symptoms of PD and ET during DBS surgery. These movement disorders and

their changes should be monitored to obtain the optimal electrode target position and stimula-

tion intensity during intraoperative stimulation. It is also supposed to support choosing the

optimal stimulation settings of the DBS electrodes after DBS surgery.

In this study, a new concept for quantifying neurological symptoms during DBS surgery was

developed, specifically concerning portability, wearability, and intraoperative application. In

this concept, the current possibilities of inertial sensor technology and motion-tracking algo-

rithms could be used to implement quantitative assessments of the primary neurological

symptoms without disrupting existing DBS processes. User-friendly human-machine interfac-

es were designed to monitor the changes of symptoms.

Introduction

2

1.2 Structure of the Thesis

The organization of this thesis is as follows.

Chapter 2 describes the motivation of this study, both for clinical and technical needs. The

introduction of PD, ET, and DBS are presented in this chapter.

Chapter 3 reviews the intraoperative assessment approaches of neurological symptoms and

the systems that can be used to quantitatively assess these neurological symptoms. Inertial

sensors and sensor fusion methods for motion tracking are also introduced in this chapter.

Chapter 4 describes the parameters, goals, and expected advantages of the glove monitoring

system. Its system concepts are presented in Chapter 5, which includes static and dynamic

system descriptions. The quantification algorithms of neuromotor symptoms are based on

statistical analyses, especially the estimation theory and its least squares methods.

For the prototypical realization in Chapter 6, designs with wireless communication interfaces

are presented first. In the first step of this study, the glove monitoring system was divided into

two separate systems: one for tremor and bradykinesia assessment and the other for rigidity

assessment. Their prototypes were based on six-axis motion tracking sensors (gyroscope and

accelerometer) and force sensors. According to the operations during clinical experiments and

the suggestions of surgeons, a modified version with combined hardware and software was

developed. As a result, a prototype that could assess all three symptoms was implemented.

Chapter 7 describes the verification of analytical methods, and clinical experiments. Com-

parative experiments between these two prototypes and an electromagnetic motion-tracking

system were carried out. The prototype for tremor and bradykinesia assessment was tested

with clinical experiments in the hospital.

Finally, conclusions and future work are given in Chapter 8.

State of the Art

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2. Application Description

The information about PD, ET, and DBS surgery are presented in Chapter 2.1. The technical

need to quantify neurological symptoms is introduced in Chapter 2.2.

This chapter shows that the quantification of neurological symptoms during DBS surgery

plays a key role in the surgical treatment of PD or ET. It provides feedback to the stimulation

settings of each DBS electrode. Thus the optimal location for the electrode implantation can

be found.

2.1 Clinical Need

2.1.1 Parkinson’s Disease and Essential Tremor

It was estimated that 1% of 70-year-olds suffer from PD (Chaudhuri et al., 2011). About 10%

of PD patients are younger than 50 years old. At present about four to six million people are

PD patients. A series of studies indicated the overall prevalence of ET to be 0.4%–0.9% and

the prevalence in 60-year-olds to be 4.6% (Louis & Ferreira, 2010). Due to demographic in-

crease of the elderly population, movement disorders such as PD and ET will occur more fre-

quently in the future.

Figure 2-1: Symptoms of Parkinson’s disease (Taken from Gowers, 1886). Parkinson’s disease affects

patients in many different ways with a variety of symptoms. These symptoms can be classified as motor

symptoms, neuropsychiatric symptoms, and autonomic dysfunction.

PD occurs mainly in a patient’s hands, feet, and head. As shown in Figure 2-1, tremor

(rhythmic back and forth motion), bradykinesia (slowness of motion), rigidity (resistance to

movement), poor balance, and parkinsonian gait are the five motor symptoms of PD

(Jankovic, 2013). Poor balance and parkinsonian gait, however, are hard to assess during DBS

surgery. Tremor is a central symptom of PD, and is generally judged according to hand tremor

with a particular frequency (3.5–7.5 Hz) and amplitude (speed and range) (Salarian et al.,

2004). Bradykinesia is a feature of basal ganglia disorders. It involves difficulties with plan-

ning, beginning, and executing movement; and with performing sequential and simultaneous

tasks. Rigidity means increased muscle tone, which is defined as a resistance to a passive

movement.

State of the Art

4

An ET can be difficult to distinguish from a parkinsonian tremor. However, ET generally pre-

sents itself with a higher frequency (4–12 Hz) and occurs only when the affected muscle is in

an active state. Physical or mental stress will also make ET worse. Therefore, rest tremor is

usually not part of the ET. In addition, the target areas of an electrode for ET and

parkinsonian tremor are different. The assessment method for both ET and parkinsonian

tremor, however, can be almost the same (Fekete et al., 2010).

Problem: Reduce the Severity of Symptoms

The mechanism responsible for PD and ET is not clear now. The primary goal in PD and ET

treatment strategies is to maintain a patient’s independence and quality of life, and at the same

time minimize potential complications of treatment. The challenge of the PD and ET treat-

ment strategies is to reduce the severity of symptoms.

At first, PD patients are treated with Levodopa medications. However, drug efficacy decreas-

es in later stages, together with the period of drug medication. For the patients with severe

symptoms of PD or ET, who are insensitive to medications, DBS is the most effective treat-

ment (Kringlbach et al., 2007).

2.1.2 Deep-Brain Stimulation Surgery

DBS was approved by the Food and Drug Administration (FDA) as a surgical treatment for

ET in 1997 and for PD in 2002. DBS can only ease some symptoms of a patient with PD or

other neurological disorders. Medications for the patient are needed even after DBS surgery

(Bittar et al., 2005). More than 80 000 DBS surgeries had been performed worldwide up until

2011 (Oluigbo et al., 2012).

Figure 2-2: DBS system (© Medtronic, 2009). Left: DBS system diagram; middle: DBS clinician pro-

grammer (size: 22cm × 10cm × 4cm); right: DBS patient programmer (size: 9.4cm × 5.6cm × 2.8cm).

Meditronic Inc. is the major manufacturer of DBS devices.

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5

A deep-brain stimulation system includes neurostimulators (a surgically implanted, battery-

operated medical device), electronic leads (also called electrodes, two thin, insulated wires for

each side of the brain), and extensions. These components can be placed in either one or both

sides of the brain. They are implanted inside the body and powered by batteries. DBS uses a

neurostimulator, which is similar to a heart pacemaker and approximately the size of a stop-

watch, to deliver electrical stimulation to targeted areas in the brain that control movement,

thus blocking the abnormal nerve signals that cause motor symptoms. As Figure 2-2 shows,

there are also two programmers which can be used by the surgeon or the patient outside the

human body. The stimulating mode, intensity and frequency of the neurostimulators can be

adjusted using the clinician programmer. The patient programmer can be used to turn the

therapy on or off.

Current Application Flow of DBS Surgery

At first, a stereotactic head frame is placed on the patient’s head to precisely target the brain

structure. The neurosurgeon uses magnetic resonance imaging (MRI) or computed tomogra-

phy (CT) scanning to identify and locate the exact area (coordinate) within the brain in which

electrical nerve signals generate the PD or ET symptoms. A three-dimensional (3D) offline

graphics program provides detailed information on the function of the area (Kringlbach et al.,

2007).

Then the patient enters the operating room and lies on a surgical bed in a reclined and com-

fortable position. The head frame is fixed to the table. In general, at least five members of the

hospital staff are required in the operating room, including a surgeon, a nurse, an anesthesiol-

ogist, a technician, and an assistant, during DBS surgery. The installation of the components

is under the condition of general anesthesia (Bittar et al., 2005).

As Figure 2-2 shows, a surgeon has drilled two small holes in the patient’s skull and has

threaded two insulated wires, with electrodes attached at the top of the brain. Lead wires are

run under the skin from the brain to the upper chest. The lead wires are attached to two

neurostimulators that are surgically implanted in the chest and provide electricity to stimulate

the brain. Two electrodes and neurostimulators on both sides are implanted one by one by

using the same surgical procedure.

Each neurostimulator sends electrical pulses to one of the three active areas of the brain via

the electrode to interfere with neural activity. There are a few target sites inside the brain for

achieving differing results, and the most common sites are the subthalamic nucleus (STN) and

the globus pallidus interna (GPi). In addition, the caudal zona incerta and the pallidofugal

fibers medial to the STN are being assessed in some situations. Then a site will be chosen for

each electrode based on the individual patient (Bittar et al., 2010).

Challenge: Select an Optimal Electrode Position

An optimal electrode position is significant for the effect of surgery. The implantation of elec-

trodes is performed only one side at a time. Before the surgery, the coordinates for the elec-

trode target area inside the brain, which can be seen from Figure 2-3, are identified with the

help of an optimum radiologic targeting (MRI-based) and a 3D offline graphics program.

The key point of the surgery is to define an optimal point for each electrode positioning.

For each electrode, 4 to 10 points inside the brain are tested during the surgery in order to

evaluate the electrode’s position and stimulation intensity. This is usually accompanied by

State of the Art

6

small movements in a small target area. After surgery, a neurologist also needs to adjust the

stimulation amplitude of the stimulator which is implanted in the chest of the patient for the

best treatment effect (Machado et al., 2006).

Figure 2-3: Electrode implantation (Based on Hotzheimer & Mayberg, 2012). The tips of the electrodes

are implanted one by one within the targeted brain area.

During DBS surgery, it is important to monitor the patient’s stimulation effects, based on his

or her individualized response to the stimulation. The quantified severity values of symptoms

can be used as feedback for the stimulating setting of the DBS electrode (Kern et al., 2007).

After its optimal target is found, an electrode is permanently implanted. For the electrode with

misplacement or in a suboptimal target, the positive effect of DBS cannot be reached or re-

stricted. Some patients need to adjust the electrode through another DBS surgery.

Assignment: Monitor the Severity of the Neurological Symptoms

Some surgeons may use microelectrode recording (MER), which involves a small wire that

monitors the activity of nerve cells in the target area, to more specifically identify the precise

brain target that will be stimulated.

The current standard for evaluating motor symptoms associated with PD is the Unified Par-

kinson’s Disease Rating Scale (UPDRS), a qualitative assessment that is completed by the

subjective judgement of the surgeons. Essential Tremor Rating Assessment Scale (TETRAS)

is the standard for evaluating the severity of ET. Motor symptoms are rated on a scale from 0

to 4 corresponding to normal, slight, mild, moderate, and severe (Tagliati et al., 2007).

The Hoehn and Yahr scale, Schwab and England Activities of Daily Living Scale, and Web-

ster Scale are also used for the assessment of PD. These subjective assessment ratings lead to

problems when evaluating the effectiveness of therapies for PD or ET (Pahwa et al., 2007).

At present, there is no designated instrumental method of monitoring the immediate motor

effects of DBS procedure. Nevertheless, surgeons need to get feedback on the stimulating

electrode in order to achieve optimal therapeutic efficacy. There is, therefore, a need to de-

velop a monitoring system that is able to quantify tremor, bradykinesia, and rigidity in real-

time during DBS surgery, instead of subjective assessment.

Target area

Electrode

State of the Art

7

A DBS stimulator can be turned on and programmed in three to four weeks after implantation.

The first programming session can take several hours in order to ensure the device is correctly

functioning and that various stimulation parameters (voltage, frequency of the DBS stimula-

tor, and electrodes mode) are optimally programmed.

Routine outpatient programming sessions at approximately one, two, and four months after

DBS surgery are needed for the patient. Because the neurostimulator contains a battery, it

must be surgically replaced every three to five years.

2.1.3 Special Concerns - Motor Fluctuations and Dyskinesia

About 50% of PD patients have motor fluctuations (MF) and dyskinesia after five years of

levodopa medications. As Figure 2-4 shows, motor fluctuations indicate the effective period

of certain doses is shorter all the time (this is known as “end-of-dose deterioration”). It also

means the alterations between “ON”, which is a state of good response to anti-parkinsonian

medications, and “OFF”, which is a state for patients experiencing pakinsonian symptoms.

The symptoms of a PD patient may reappear unexpectedly and quickly. This switch sensation

is described as “ON/OFF” syndrome. Levodopa-induced dyskinesia (LID) involves a series of

hyperkinetic movements (involuntary, episodic, and irregular) such as athetosis, chorea, and

dystonia (Hause et al., 2000).

Figure 2-4: Motor fluctuations and dyskinesia. Levodopa’s therapeutic window narrows overtime during

ON state. As a result, the patient’s response to Levodopa shortens over time.

Dyskinesia is one of the side effects of DBS surgery. Dyskinesia appears when the electrode

stimulating voltage is too high. Thus, it is important to avoid such over-stimulation.

2.2 Technical Need

In 1872, Jean-Martin Charcot (1825–1893), the most celebrated clinical neurologist of the

19th century, developed a tremor recording device, as Figure 2-5 shows. This device provided

a new approach to assess the symptom severity of movement disorders, other than visual ob-

servation and clinical maneuvers.

At present, several groups have used electromyography (EMG), computer tracking, digital

tablets, infrared video cameras, and laser transducers to assess tremor and bradykinesia objec-

tively. All these solutions have demonstrated limited usability in clinical settings due to defi-

ciencies in wearability, fidelity, and flexibility (Pahwa & Kvons, 2007).

ON state without LID

Peak-dose dyskinesias

Peak-dose dyskinesiasOFF state

Levodopa Administration (Date)

ON state with LID

Levodopa C

oncentr

ation (

Dose)

OFF

ON

ON &Dyskinesia

State of the Art

8

Figure 2-5: Charcot’s early tremor recording device (lower left) and its recordings (lower right). Charcot

used the sphygmograph to record tremors in the wrist. The resultant tremor recordings were conducted

at rest (AB) and during activity (BC) of a patient with Parkinson’s disease (Based on Pahwa & Kvons,

2007).

There has recently been growing interest in the application of body-fixed sensors (BFS) and

in particular inertial sensors for long-term monitoring of PD symptoms. Micro-Electro-

Mechanical Systems (MEMS) gyroscope and accelerometer are widely used for the motion

tracking in tremor and bradykinesia assessments. With the development of new MEMS tech-

nology, the dimension of the sensor circuit board is smaller and the signal processing is easier

to be carried out than before.

Other kinematic sensors and force sensor have been used to detect and quantify rigidity.

These sensors were placed on the body, feet or arms of the patients and were not specifically

designed for DBS (Patel et al., 2008).

The assessment system of neurological symptoms for DBS surgery must strictly adhere to the

requirements in the operation room. It is important to perform three assessment tasks in a

portable and wearable system with small dimensions. This system should be safe and not re-

strict the patient’s movement.

In addition, a user-friendly human-machine interface should be designed to support the sur-

geons to monitor the changes of symptoms.

A

A

A

B

B

B

C

C

C

State of the Art

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3. State of the Art

The clinical assessment tasks of tremors, bradykinesia, and rigidity is first introduced in

Chapter 3.1.

The intraoperative assessment of neurological symptoms is introduced in Chapter 3.2. At pre-

sent there are only two intraoperative approaches to assess neurological symptoms:

MER/EMG techniques and subjective assessment by the surgeons.

There are also some systems which are used to quantitatively assess neurological symptoms

outside of the operating room. Some researchers and companies have tried to assess the sever-

ity of neurological symptoms and their changes (OFF and ON state).

The quantitative assessment methods of neurological symptoms in these systems are intro-

duced in Chapter 3.3. In addition, these systems are classified into commercial systems and

systems in research and development. Detailed information of the assessment systems is de-

scribed in Chapter 3.4 and Chapter 3.5 respectively. However, most of them are for tremor

and bradykinesia assessment. Rigidity assessment is available only in a few research projects.

MEMS inertial sensors and sensor fusion algorithms for motion tracking are introduced in

Chapter 3.6.

Limitations of the existed technology (disadvantages of the state of the art) are described in

Chapter 3.7.

3.1 Neurological Symptoms and Assessment Tasks

In clinical practice, tremor assessment tasks include rest tremor, postural tremor, and action

tremor movements. Bradykinesia measurement tasks include finger tapping, hand grasping,

and rapidly alternating movements. Passive elbow or wrist movements (repeatedly flexing

and extending) are used for rigidity assessment.

A detailed instruction of the assessment tasks is described as follows.

3.1.1 Tremor Assessment

Symptom

Tremor syndromes (oscillatory movements) are classified into three primary types: rest trem-

or (RT), postural tremor, and action tremor. Rest tremor, which is the characteristic of

parkinsonian tremor, happens when a body part is relaxed, for example, when lying in bed.

Postural tremor, which is the characteristic of ET, occurs while a body part is maintaining a

position against gravity. Action tremor happens when a voluntary contraction of a muscle,

follows the condition, for example, holding a cup. In most situations, parkinsonian tremor

manifests the combination of rest, postural, and action tremors (Deuschl et al., 1998). ET oc-

curs in postural tremor and action tremor.

Parkinsonian tremor is the central symptom of PD. It is prominent in PD, about 70% of PD

cases. The tremor associated with PD has a characteristic appearance, and reduces with pur-

poseful activity. Symptoms often start with an occasional tremor in one finger that spreads

State of the Art

10

over time to involve the whole arm. ET is present when the limb is at rest or held up in a stiff

unsupported position, and usually disappears briefly during movement.

Assessment

As shown in Figure 3-1, when the rest tremor is assessed, the patient should sit quietly in a

chair or lie down in bed with his or her hands and feet comfortably placed for several seconds

with no other activity. The stable tremor amplitude gives the final score.

a) b) c)

Figure 3-1: Tremor assessment tasks: a) rest tremor; b) postural tremor; c) action tremor. When lying in

the bed during DBS surgery, the assessment tasks for the patient are the same to the general clinical ex-

ams (Based on Dai et al., 2013a).

When testing postural tremor, an examiner instructs the patient to stretch his or her arms out

with the palms facing down. The wrist should be straight and the fingers comfortably sepa-

rated so that they do not touch each other. This posture is observed for ten seconds.

Action tremor of the hands is tested by the finger-to-nose maneuver or by holding a cup. With

the arm starting from the outstretched position, the patient performs at least three finger-to-

nose maneuvers with each hand reaching as far as possible to touch his/her nose or the exam-

iner’s finger. The finger-to-nose maneuver should be performed slowly enough so as not to

hide any tremor that may occur with very fast arm movements. This is repeated with the other

hand, with each one rated separately. The tremor may be present throughout the movement,

and the highest stable tremor amplitude is rated.

The patient needs to completely relax during the rest tremor task because the tremor may be

heightened by the mental load. The patient should outstretch two arms during a postural

tremor task. Drinking, spoon, and spiral movements can be chosen as additional action tremor

tasks.

3.1.2 Bradykinesia Assessment

Symptom

Bradykinesia often initially manifests as slowness in performing activities. It is the most char-

acteristic feature of PD and is often associated with an impaired ability to adjust the body’s

position (Jankovic et al., 2008).

Bradykinesia encompasses slowness, decreased movement amplitude, and dysrhythmia. It

means the inability of generating maximum speed, power, or force. According to the modified

bradykinesia rating scale (MBRS), the speed, amplitude, and rhythm of bradykinesia task rep-

resent the parameters of bradykinesia, hypokinesia, and dysrhythmia, respectively.

Hypokinesia refers to a decreased amplitude or range of bodily movement. In addition, the

difficulty in selecting or activating motor programs in the CNS may result in akinesia (inabil-

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11

ity to initiate movement) in the patient’s daily life. Akinesia (absence of movement) during

bradykinesia task is the delay (action time) of the patient to start the assessment task after the

instruction from the examiner.

Assessment

Bradykinesia measurement tasks include finger tapping, hand grasping, and rapid alternating

movements (Heldman et al., 2008).

Figure 3-2: Finger tapping.

As shown in Figure 3-2, finger tapping is used to assess bradykinesia. The subject is in-

structed to tap his/her fingers in rapid succession as quickly and as widely as possible. After

five seconds of practice, such open-close movement is performed for several seconds or sev-

eral times. Speed, amplitude, halts, and any decline in amplitude are evaluated. At least two

contact sensors on two fingers are used for the measurement of tapping duration. In order to

measure the range of the finger tap actions, two gyroscopes and two accelerometers are also

used when finger tapping is adopted.

Figure 3-3: Supination-pronation movements of the hand.

As shown in Figure 3-3, supination-pronation movement is also used to assess bradykinesia.

The patient is instructed to extend the arm out in front of his/her body with the palms facing

down and then to turn the palm up and down alternately several times (or several seconds) as

fast and as fully as possible.

Figure 3-4: Whole-hand grasping (Based on Dai et al., 2013a).

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12

Whole-hand grasping is another movement used to assess bradykinesia. Figure 3-4 shows the

bradykinesia task, when all the fingers are closing and opening repeatedly. A single closing

and opening movement is regarded as a grasp cycle. The subject is required to grasp with the

greatest possible range and frequency. A single assessment task lasts for 10 to 15 seconds.

For supination-pronation and whole-hand grasping movements, a triple-axis gyroscope is

necessary to measure the angular displacement of the hand or finger. The gyroscope signals

obtained from patients with mild bradykinesia have a consistent amplitude and frequency and

appear sinusoidal. However, signals from patients with severe bradykinesia have much lower

and inconsistent amplitude and frequency. Speed, amplitude, halts, hesitations, and any de-

cline in amplitude are evaluated.

3.1.3 Rigidity Assessment

Symptom

Rigidity responds immediately upon PD treatment. It refers to a permanently elevated muscle

contraction, independent of passive movement velocity. Patients with severe rigidity can

hardly reach muscle relaxation and their voluntary movements are accompanied by an ele-

vated contraction of antagonist muscles (Shapiro et al., 2007).

Cogwheel rigidity, which means the muscles perform ratchet jerks when passive force bends

the limb, always appears as an early sign of PD. It performs a cogwheel mechanism with the

frequency range from 6 to 9 Hz and depends on the stretch velocity. This phenomenon seems

to be the combination of rigidity and superimposed tremor, which has a higher frequency than

parkinsonian tremor.

Assessment

Hand rigidity is more difficult to measure, and there is currently no standardized objective

method for measuring rigidity. Quantification of the mechanical properties of a joint can be

realized by passive joint movement, for example, flexion and extension of the joint by a clini-

cian or a torque motor. Rigidity is commonly assessed in the upper limbs at the wrist or el-

bow.

Figure 3-5: Elbow flexion-extension. An examiner holds the elbow of the patient for passive flexion-

extension movement. The examiner first puts one hand on the fixpoint. At the same time, his/her other

hand holds the patient’s wrist, on both sides, to perform flexions and extensions of the patient’s elbow.

As Figure 3-5 shows, passive flexion and extension of the elbow is used to assess rigidity.

The examiner flexes and extends the elbow joint through the force at the wrist. In clinical

practice, each test lasts for 10 to 30 seconds.

Fixpoint

Force point

Force point

State of the Art

13

3.2 Intraoperative Assessment of Neurological Symptoms

Subjective Assessments

The assessment of rest tremor (RT), action tremor, and postural tremor in the clinic is current-

ly mainly based on subjective methods and MER techniques according to clinical scales

(UPDRS and TETRAS) (Elbe et al., 2010).

When assessing bradykinesia during the DBS surgery, a neurologist asks the patient to per-

form rapid, repetitive, and alternating movements of the hand. The slowness level of the mo-

tion is scored according to the UPDRS. Based on experiences, the examiner classifies

bradykinesia severity on a four-point scale from 0 to 4 (Salarian et al., 2007).

In clinical practice, rigidity assessment is realized through passive movement of the subject’s

limb, which is controlled by a neurologist or another examiner. The level of instinctive re-

sistance to the exerted movement is scored according to the UPDRS ratings. Based on experi-

ences, the examiner classifies rigidity on a scale from 0 to 4, which is compared to a control

group (Patrick et al., 2001).

Quantitative Assessments: EMG and MER

At present, the Electromyography (EMG) method is used to measure tremor during DBS sur-

gery in some hospitals. The EMG recording technique allows the detection and monitoring of

electrical muscle activity following the attachment of surface electrodes to the surface of se-

lected muscles (Rissanen et al., 2007).

Figure 3-6: ISIS MER system (© inomed, 2012).

In addition, intraoperative microelectrode recording (MER) facilitates the surgeon in targeting

the optimal placement of each electrode. The surgeon connects small microelectrodes into the

intended target area and observes the pattern of neuronal activity to physiologically confirm

the optimal stimulating position of the electrode. The small tips of the microelectrodes are

placed close to the DBS electrode. They acquire the electrode activity (5100 µA) of individ-

ual neurons at a very high frequency (300 Hz) (McClelland et al., 2011).

Figure 3-6 shows the ISIS MER system with additional EMG function from inomed GmbH,

Germany. It includes a monitor, operating panel, main isolation unit, computer, printer, loud-

State of the Art

14

speaker, ISIS headboxes, ISIS neurostimulator, keyboard, mouse, and a drawer for accesso-

ries.

Figure 3-7: Electrode implantation and intra-operative MER recording during DBS surgery at the Uni-

versity Hospital of Munich (LMU) (© Mehrkens LMU, 2011). a) electrode implantation; b) MER graph-

ical user interface.

Figure 3-7 shows the five-channel MER recording during a DBS surgery at the University

Hospital of Munich (LMU, Germany). At the same time, there was an audio feedback of the

relative neuronal activity.

Because there are differences between different patients’ brains, the information obtained

from MER provides a more accurate target as final DBS placement than the information from

the MRI figures. Surgeons and technicians visualize and hear the neuronal activity from dif-

ferent points of the target area to identify specific structures based on the unique patterns of

neuronal activity (Bittar et al., 2005).

1

2

3

1) Surgeon

2) Sterotactic frame

3) Patient

Sound button MER signal

a)

b)

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15

At the same time, the surgeon may move the patient’s joints or ask the patient to perform

physical examinations. The action of holding a cup or other objects by the patient is usually

assessed to help detect hand tremor. Fast hand movement is adopted for bradykinesia assess-

ment. Passive movement of the patient’s joint is used for rigidity assessment. The surgeon

assesses the symptom severity according to the clinical ratings based on experience.

The technician working with the MER or EMG equipment records the level of symptom se-

verity when the electrode, after being implanted within the target brain area, is slowly moving

within the brain. The surgeon can precisely map the target area (sensorimotor portion) in this

way and the optimal location of the electrode is identified (Rissanen et al., 2011).

Interventional MRI

Recently, an interventional MRI (or iMRI) with real-time imaging has appeared that has a

higher target accuracy than the MER system during the surgical procedure, which as a result

shortens the procedure time, even without a MER system (Ostrem et al., 2013). However, as

it is available in only a few hospitals, we do not discuss it in this study.

3.3 Quantitative Assessment of Neurological Symptoms

The summaries of quantitative assessment methods of tremor, bradykinesia, rigidity are listed

from Table 3-1 to Table 3-3, respectively. The quantitative assessment method of dyskinesia

is introduced in Chapter 3.3.4.

3.3.1 Tremor Quantification

Figure 3-8: Representative segments of the normalized time series of tremor signals (Based on Timmer et

al., 2000a). a) parkinsonian tremor; b) essential tremor. These data were recorded from the main direc-

tion (up and down) of the tremors by the usage of a single axis piezoresistive accelerometer. These types of

sensor data are usually sufficient for the analysis of ET but are insufficient for parkinsonian tremor be-

cause parkinsonian tremor is the movement of more than one dimension.

0 2 4 6 8 10

-2

-1

0

1

2

Acce

lera

tio

n (

g)

Time (s)

0 2 4 6 8 10

-1

0

1

2

3

Acce

lera

tio

n (

g)

Time (s)

a)

b)

State of the Art

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Tremor amplitude and frequency (3.5–7.5 Hz for parkinsonian tremor; 4–12 Hz for ET) are

the important features (Elble et al., 2011).

Tremor is the most apparent and well-known symptom of PD. According to the patient data

from Prof. Dr. Jens Timmer at Freiburg University, representative segments of normalized

time series of essential tremor and parkinsonian tremor are shown in Figure 3-8.

Timmer et al. revealed that both parkinsonian tremor and ET exhibit a 2-order nonlinear os-

cillation, which is not strictly periodic (Timmer et al., 2000).

Table 3-1: Summary of tremor assessment methods. Peak power represents the power estimation

around the dominant frequency in the power spectrum of sensor signals; RMS refers to the root mean

square value; and “−” denotes no exact information.

Company or

author

Tremor Assessment Systems

Device or

system Sensors

Sensors

position Amplitude parameter

– Examiner Sight of the sur-

geon – UPDRS score

Inomed GmbH ISIS MER MER needle Brain Neuronal activity (voltage)

Giuffrida et al.,

2009 Kinesia

EMG,

gyroscope,

accelerometer

Finger Peak power

Burkhard et al.,

2002 MOTUS Gyroscope Palm Peak power

Narcisa et al.,

2011 CATSYS Accelerometer Hand RMS of accelerations

Patel et al.,

2009 Shimmer

Gyroscope,

compass Body Data range

Synnott et al.,

2012 WiiPD

Accelerometer,

infrared Hand RMS of accelerations

Salarian et al.,

2007 ASUR Gyroscope Wrist RMS of angular velocities

Spyers-Ashby

& Stokes, 2000 FASTRK

Electromagnetic

sensor Hand RMS of displacement

An overview of recent approaches in tremor assessment is given in Table 3-1. These sensor-

based systems could access the alternate between therapy “OFF” and “ON” states during

stimulation and medication. Most tremor assessment methods were based on the inertial sen-

sors (gyroscope and accelerometer) and a computer-based system. Sensors were placed on the

body, feet or arms of the patient.

More specifically, quantification of tremor has been achieved by numerical methods such as

time-domain analysis, spectral analysis, time-frequency analysis, and nonlinear analysis.

According to the research by Elble et al. (2006), which enrolled 928 patients, hand tremor

amplitude is logarithmically related to the 5-point clinical tremor ratings. According to the

research by Giuffrida et al. (2009), for the rest and postural tremor in PD, the summation of

logarithm peaks in both the power spectrums of accelerometer and gyroscope data has the

highest correlation with UPDRS scores, while the coefficient of determination (r2) was

around 0.9. For the action tremor in PD, the root-mean-square (RMS) sum of both gyroscope

State of the Art

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and accelerometer data has the highest correlation with UPDRS (r2 = 0.69) (Mostile et al.,

2010).

3.3.2 Bradykinesia Quantification

Bradykinesia is quantified through finger tapping, whole-hand grasping, and supination-

pronation movements of hands.

Several groups and companies have used computer tracking, digital tablets, infrared video

cameras, or laser transducers in order to objectively assess parkinsonian bradykinesia.

An overview of recent approaches in bradykinesia assessment is given in Table 3-2.

Table 3-2: Summary of bradykinesia assessment methods. Here “–” denotes no exact information.

Company or

author Joint/ Task

Angle

measurement Parameters

– Examiner Sight of the sur-

geon –

Inomed

GmbH ISIS MER MER needle Neuronal activity (voltage)

Niazmand et

al., 2011a

Fingers/

Finger taps

Accelerometer,

touch sensors

Average and standard

deviation of the duration

Salarian et al.,

2007

Wrist/

Postural move Gyroscope Speed, amplitude, rhythm

Heldman et

al., 2011

Hand/ Tap, grasp&

pronation-supination

Accelerometer,

gyroscope, EMG Speed, amplitude, rhythm

Kim et al.,

2011a Fingers/ Finger taps Gyroscope Root-mean-square, cycle

Su et al.,

2003 Hand/ Grasps

Electromagnetic

sensors Speed, frequency

Kim et al. (2011a), from Konkuk University, Korea, quantified parkinsonian bradykinesia

during finger taps using a gyroscope. RMS velocity, RMS angle, and the estimated power

around dominant frequency were correlated well with clinical finger tapping scores.

3.3.3 Rigidity Quantification

For the quantitative assessment of parkinsonian rigidity, there are no available devices on the

market. According to research conducted by Patrick et al. (2001), expense, complexity, and

time involved are the most common reasons for not introducing quantitative rigidity evalua-

tion in clinical praxis.

Some research projects have tried to explore the relationship between biomechanical parame-

ters and the UPDRS rigidity scale. In most research, an examiner or a motor drive flexes and

extends a joint repeatedly, and then parameters from the applied torque are calculated. How-

ever, there are also researchers who calculate rigidity parameters from the electromyographic

potentials during flexion-extension movement. Patrick’s research indicated that the correla-

tion of mechanical properties with the UPDRS scores is superior to the correlation of EMG

with the UPDRS scores (correlation coefficient r: 0.60–0.86 compared to 0.37–0.79) (Sakoda

et al.; Patrick et al., 2001).

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Table 3-3: Summary of rigidity assessment methods. Here “+” denotes the system performance in a

certain aspect, the amount of “+” represents a better performance; and “–” denotes no exact infor-

mation available.

Methods Assessed

joint

Inter-

variability

Intra-

variability Parameter

Signal

process Size

Fixed

frequency

Device or

reference

Feeling Elbow + ++ Feeling of

examiner – – No

Examiner

test

Electrical

current Brain +++ +++

Neuronal

activity + + – ISIS MER

Electrical

current

Hand/

Foot +++ +++

Muscle

activity + + – EMG

EMG;

torque-

angle

Wrist +++ +++ Work ++ + Yes Shapiro et

al., 2007

Torque-

angle Elbow ++ +++

Mechanical

impedance +++ +++ No

Patrick et

al., 2001

Torque-

angle Elbow +++ +++

Viscoelastic

values ++++ ++ Yes

Park et al.,

2011

Force-

angle Elbow ++ +++

EMG,

torque bias ++++ +++ No

Endo et

al., 2009

Torque-

angle Elbow ++ +++

Viscoelastic

values ++++ ++ No

Prochazka

et al., 1997

Some systems are designed to model the relationship between changing joint angles (degree)

and measured torque (N·m), which includes non-neural torque and neural torque. Force or

torque transducer, EMG, and position or angle sensors are used. Some approaches use kine-

matics to restrict the movement of the limbs, while others do not. An overview of recent ap-

proaches is given in Table 3-3.

A potentiometer is easy to use for angle estimation. However, a potentiometer requires the

examiner to strap the patient’s limbs to some kind of cinematic device.

3.3.4 Dyskinesia Quantification

Figure 3-9: Signals of a triple-axis gyroscope and its power spectrums during an arms-extended task. This

figure describes how the features change as a patient experienced one cycle of levodopa-induced dyskine-

sia. Tremor was apparent in hours 1 and 3, while the dyskinesia was experienced in hour 2 (Based on

Mera et al., 2013).

The research conducted by Burkhard et al. (1999) shows that the RMS of the power spectrum

Time domain Frequency domain Clinical scores

0 2

2 0

0 1

Dyskinesia Tremor

(s)

Hour 2 Hour 2

2

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of hand-attached gyroscope signals, from 0.25 to 3.25 Hz, during dyskinesia task correlated

well with the five-point clinical ratings for dyskinesia severity.

As Figure 3-9 shows, Mera et al. (2012) revealed that the RMS of the frequency band, from 0

to 3Hz, from all the three gyroscope channels correlates well with the modified Abnormal

Involuntary Rating Scale (m-AIMS). Two stationary motor tasks were performed during the

arms-extended task: arms resting and arms extended. The dyskinesia severity was calculated

based on the inertial sensor signal’s range from 0 to 3 Hz. However, the algorithm could not

be applied to voluntary motor tasks.

3.4 Commercial Systems for Quantification of Neurological Symptoms

Kinesia™ (CleveMed Inc., USA) is a compact, clinical device that is used to objectively

quantify the motor symptoms of movement disorders such as PD and ET.

Figure 3-10: Kinesia™ (old version) for tremor and bradykinesia assessments (© CleveMed, U.S.A, 2010).

On the left side of the figure, a subject performed the assessment task according to the video instruction.

The right side of the figure shows the hardware of a Kinesia™ system.

a) b) c)

Figure 3-11: Kinesia™ (new version) for tremor and bradykinesia assessments (© Great Lakes

NeuroTechnologies, U.S.A, 2013). a) hardware and GUI; b) tremor tuning map for a patient during an

outpatient programming session; c) bradykinesia tuning map for a patient during an outpatient pro-

gramming session. The voltage is the stimulation intensity of the electrode. The color in the tuning maps

means the severity of the symptoms from red (most severe) to green (no symptoms). CleveMed Inc. spun

Great Lakes NeuroTechnologies Inc. in 2011, which focuses on the assessment of motor symptoms based

on inertial sensors.

As shown in Figure 3-10, a Kinesia™ was worn on the finger and wrist of the patient while

symptom information was wirelessly transmitted to a nearby computer for display, analysis,

automated symptom severity scoring, report generation, and storage. Three orthogonal gyro-

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20

scopes and three orthogonal accelerometers were placed on the finger, to capture motion with

six degrees of freedom (DOF). This device can be used by PD patients to monitor the kine-

matics of motor symptoms such as tremor and bradykinesia. Several time- and frequency-

based parameters are computed for each kinematic channel (axis) including peak power, fre-

quency of the peak power, root mean square (RMS) of the angular velocity, and RMS of the

angle. This device has been approved by the FDA (Giuffrida et al., 2009). KinetiSense, an

upgraded version of Kinesia™, is a small, lightweight, wireless device that integrates motion

detection and EMG. Three orthogonal accelerometers and gyroscopes provide 3D motion

tracking while two EMG channels record muscle activity.

As shown in Figure 3-11, Great Lakes NeuroTechnologies Inc. released the new generation of

Kinesia™ in 2013, which can generate functional motor symptom response tuning maps. The

wireless ring sensor communicates with the tablet computer via a Bluetooth interface. Tuning

maps can be used to monitor the stimulation setting after DBS surgery for a particular symp-

tom or averaged across multiple symptoms. Its scoring algorithms are clinically validated.

HandTutor™ (MediTouch Inc., Israel) is a state-of-the-art glove and software that enables

intensive clinic- or home-based hand rehabilitation (quantitative assessment and customized

training). As shown in Figure 3-12, it evaluates the extension/flexion of the fingers and wrist.

It comprises six triple-axis accelerometers, five on the fingers and one on the wrist. The data

recorded by the device include motion locus, spectrum, maximum velocity, and ROM (range

of motion) (Carmeli et al., 2009).

Figure 3-12: HandTutor™. Electro-optical sensors, bend sensors, and accelerometers are placed in the

upper side of each finger. The training parameters of each finger are displayed in the GUI. It is power

supplied by the USB interface of a computer (© MediTouch, 2010).

The Motus Movement Monitor (MOTUS Bioengineering Inc., USA) can be used to monitor

the patient’s responses for the electrode implantation during DBS surgery. As shown in Fig-

ure 3-13, only one gyroscope is used on the surface of the palm. The Motus system assists the

neurologist in the selection process by providing quantification of movements, clearly docu-

menting tremor characteristics, the extent of bradykinesia (via pronation/supination move-

ments), and the prominent side of the disorder.

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21

Figure 3-13: Motus Movement Monitor. The raw data, Fast Fourier Transform (FFT) chart and parame-

ters are displayed in the GUI (© MOTUS, 2010).

In addition, glove-based hand motion tracking systems represent an appropriate method aimed

at acquiring hand movement data. There are many products currently available on the market

that can achieve these or similar goals. These include the Cyberglove, P5 Glove™, 5DT

Dataglove, Acceleglove, Hand Mentor, and HandTutor. They allow continuous tracking of

hand joint angles and linear displacements.

3.5 Research Systems for Quantification of Neurological Symptoms

Su et al. (2003) of the University of East Anglia, UK, developed a three-dimensional motion

recording system (data-gloves) for PD assessment. As shown in Figure 3-14 (a), this system

uses 11 electromagnetic sensors on the hand. Time traces, frequency analysis, and speed

analysis of these time traces are the key parameters. Tremor parameters, the rigidity of the

wrist during rolling movements, the dexterity of finger pinching, and hand-gripping move-

ments are recorded. As shown in Figure 3-14 (b), they later used EMG together with the pre-

vious motion system to obtain better results (Su et al., 2003, 2007).

a) b) c)

Figure 3-14: 3D motion recording system. a) sensor layout; b) simultaneous recording system; c) user

graphical interface of the simultaneous data recording (Taken from Su et al., 2007).

Arash of the EPFL1, Switzerland, designed a new measurement system consisting of five in-

1 Ecole Polytechnique Fédérale de Lausanne

State of the Art

22

dependent, lightweight, autonomous sensing units, which were based on gyroscopes that can

continuously record body movements during daily life. An accurate algorithm based on spec-

tral estimation was proposed to detect and quantify tremor during the daily activities of PD

patients with a resolution down to three seconds using gyroscopes attached to the forearms.

This system, which is shown in Figure 3-15, also successfully detected ON and OFF periods

in PD patients under ambulatory conditions (Arash et al., 2006).

a) b)

Figure 3-15: Ambulatory system for quantification of tremor and bradykinesia. a) the sensitive axes of the

3D gyroscope; b) a closer photo of the module (Taken from Salarian et al., 2006).

Bamberg from MIT (Massachusetts Institute of Technology), USA, built a shoe-integrated

sensor system for wireless gait analysis. She used gyroscopes, accelerometers, and force sen-

sors for the calculation of gait parameters. Detailed information on the gait cycle was ob-

tained. Her PhD thesis described the signal processing methods of gyroscopes and acceler-

ometers in great detail (Bamberg et al., 2008).

Funded by EU Framework 7, and coordinated by Prof. José Luis Pons of the Bioengineering

Group, CSIC (Spanish National Research Council), Spain, the tremor project team is develop-

ing an ambulatory brain-computer-interface-driven tremor suppression system based on func-

tional electrical stimulation. This system will be used to detect and monitor tremor through a

multimodal brain-computer-interface (BCI). The proposed BCI method will combine CNS

signals (Electroencephalography, EEG) and peripheral nervous system signals (Electromyog-

raphy, EMG) data with biomechanical data (inertial sensors) in a sensor fusion approach. It

will model and track both tremor and voluntary motions (Ibánez et al., 2010).

Figure 3-16: Validation of elastic stiffness (K) calculations using a model arm. The quantification device

was tested on a prosthetic limb to which combinations of elastic cords were attached to produce different

levels of constant stiffness. An examiner flexes and extends the subject’s joint. A gyroscope in the arm was

used to measure the elbow angular displacement, while a differential force transducer attached to the

wrist was used to monitor the amount of force employed (Based on Patrick et al., 2001).

Load cell and LVDT (linear variable displacement transducer):

Linear regression-calculated K (N·m/degree)

Validation of elastic stiffness (K) calculations

Quantification d

evic

e K

(N·m

/deg

ree)

State of the Art

23

Patrick et al. (2001), from the University of Alberta, Canada, have developed a stiffness quan-

tification device. As shown in Figure 3-16, the device is based on two air-filled pads held dis-

tal to the joint and a gyroscope mounted on one of the force pads. Both pads are connected to

a differential force transducer. Measurements of mechanical impedance (the magnitude of the

vectorial sum of elastic stiffness and viscous stiffness) corresponded well with the clinical

ratings of parkinsonian rigidity.

Other researchers have designed mechanical devices to simultaneously measure the torque

and angular position of the elbow or wrist joint during flexion-extension movement (Shapiro

et al., 2007).

Sepehri et al. (2007), from the Islamic Azad University-Mashhad Branch, Iran, presented a

test rig to measure the range of motion, and viscous and elastic components of elbow stiff-

ness. As Figure 3-17 shows, the subject’s elbow joint was fixed in the mechanical test rig. A

balanced strain gage force transducer and a 10 K potentiometer were used to measure at-

tached force and angular displacement, respectively. Their results revealed that the elastic and

viscous components of mechanical impedance measured with the device correlated well with

the UPDRS ratings.

Figure 3-17: Mechanical rig test system and its operation (Taken from Sepehri et al., 2007). The elbow

movement was driven by an examiner.

As Figure 3-18 shows, Park et al. (2011), from the Korea University, analyzed the wrist’s

viscoelastic properties in PD patients. Their study suggested the mean viscosity during both

flexion and extension correlated best with the clinical rigidity score.

Figure 3-18: System structure and experimental setup for the analysis of the wrist joint’s visco-elastic

properties (Based on Park et al., 2010). The wrist movement was controlled by a motor.

Handle

Load cell

Hand plate

Half-circle plate

Lower arm plate

Potentiometer

Cotton-buffered splint

Handle

Load cell

Accelerometer

Potentiometer

State of the Art

24

As shown in Figure 3-19, Niazmand et al. (2011a), from the TU Muenchen, Germany, pre-

sented a smart glove for quantitative evaluation of PD symptoms. Three primary symptoms

could have been assessed. However, there was no correlation between the clinical rigidity

severity and force sensor value. In addition, there was no discussion of tremor amplitude. The

bradykinesia assessment task was finger taps.

Figure 3-19: Smart glove for quantitative evaluation of PD symptoms (Taken from Niazmand et al.,

2011a). 1) force sensor; 2) command module; 3) sensor board; 4) touch sensor (positive connector); 5)

touch sensor (ground connector). The force sensor was attached to the surface of the command module.

Signal Processing Methods

In these products and research projects for the quantification of tremor or bradykinesia severi-

ty, a fast Fourier transform (FFT) or power spectral density (PSD) calculation in real-time is

carried out. Fuzzy analysis, a machine-learning algorithm, and other algorithms are used for

frequency and amplitude analysis. Regression analysis is used in most rigidity assessment

systems.

3.6 Inertial Sensors and Sensor Fusion for Motion Tracking

3.6.1 Inertial Sensors and Inertial Measurement Unit

Some types of motion sensors have been commercially available for several decades in appli-

cations for ships, aircraft, and automobiles. However, these sensors’ characteristics, such as

dimension, power consumption, and price, have prevented their wide utilization in consumer

electronic devices up until the past few years (Shaeffer, 2013).

Accelerometer (G-sensors), gyroscope, magnetic sensor (E-compass), and pressure sensors

(barometers) are the four fundamental motion sensors for mobile device and other consumer

electronics.

Accelerometers measure linear acceleration (dynamic acceleration) and tilt angle (static ac-

celeration) with limited motion sensing functionality. Gyroscopes measure the angular veloci-

ty in one or more axes with high signal-to-noise ratio (SNR). Gyroscopes can accurately

measure complex rotational motions in free space. Another difference between gyroscopes

and accelerometers/compasses is that gyroscopes function fairly autonomously, other than

depending on any external forces such as magnetic fields or gravity. Compasses detect only

the heading of a triple-axis space based on the earth’s magnetic field. Pressure sensors realize

relative and absolute altitude by sensing the relationship analysis between atmospheric pres-

sure and altitude.

Using a gyroscope or an accelerometer as the single source for angle calculation, also shows

disadvantages (Patel et al., 2008; Okuno et al., 2009). The output of a gyroscope is read at

State of the Art

25

certain time intervals. Thus, the information between these periods is missed. In order to im-

prove the accuracy of angular displacement, more gyroscope samples are needed, which takes

more processing time. Due to the inaccuracy of each gyroscope reading, the angular dis-

placement calculated will drift over time. The angle calculation with accelerometer data is

based on gravity. The accelerometer will give an accurate reading of tilt angle in a static state.

But accelerometers are slower to respond than gyroscopes and are prone to vibration or noise.

An accelerometer can be used to correct gyroscope drift errors and is more sensitive to non-

rotation movement.

Another type of sensor commonly used to reduce drift is the magnetometer, which measures

magnetic field strength in a given direction. The magnetometers measure the strength and

direction of the local magnetic field, allowing the north direction to be found. The yaw an-

gle is calculated from the magnetic field.

The silicon MEMS-based technology reduces the cost and package size of an inertial sensor.

At present, most analog inertial sensors are replaced by digital inertial sensors, which means

that there is no analog-to-digital converter (ADC) outside the sensor anymore. In addition,

three axes sensors are combined in a single chip. Programmable filter and full-scale range

setting are also available inside the chip. All these features give MEMS-based sensors better

noise performance than before.

Motion processing with MEMS technology, which measures and intelligently processes the

movements of subjects in three dimensional spaces, is the next major revolutionary technol-

ogy that will drive innovation in mobile device design, human-machine interface design, and

applications for navigation and control. Consumer-grade IMUs based on MEMS provide a

simpler user interface for intuitionistic navigation and control of handheld mobile devices.

Due to the advantages of IMUs, the operational complexities that have confused many owners

of sophisticated consumer electronic devices can be resolved (Shaeffer, 2013).

Over the past 30 years, inertial sensors have been used in major automotive and industrial

markets. With the development of mobile devices, especially the revolutionary iPhone and

iPad from Apple Inc., U.S.A, motion MEMS sensors with low-cost, ultra-compact, low power

consumption and multiple-axis sensing have been developed in last five years. ST,

Invensense, Bosch, Freescale, and Kionix are the dominant motion MEMS manufacturers at

present.

InvenSense claimed it was the first company to develop an integrated triple-axis MEMS gyro-

scope and six-axis motion-tracking device, with digital-output, for consumer electronics ap-

plications. In February 2011, Invensense Inc. launched MPU-6000, which integrates three

accelerometers and three gyroscopes into a single package (4mm 4mm 0.9mm). In August

2011, nine-axis motion-fusion algorithms (together with a triple-axis compass outside con-

nected) and an upgraded version (MPU6050) were presented.

3.6.2 Accelerometer-Magnetometer and Attitude Heading Reference System

A six-axis accelerometer-magnetometer unit, which contains a triple accelerometer and a tri-

ple magnetic sensor with about 0.6 mA of power, accurately determines heading and orienta-

tion. Some companies regard this type unit as a simulated IMU. However, this unit can only

measure slow changes in a three dimensional space.

State of the Art

26

Gyroscopes and accelerometers are great, but they cannot provide precise and accurate calcu-

lations such as the absolute heading value. An attitude heading reference system (AHRS) con-

sists of a triple-axis gyroscope, a triple-axis accelerometer and a triple-axis magnetometer

(compass). The use of the compass, which provides a heading reading, offers enhanced angu-

lar position accuracy, and reduces gyroscope drift.

Raw output from multiple discrete sensors requires development and incorporation of a com-

plex set of sensor fusion algorithms, calibration firmware, and performance testing prior to

use (Paces & Popelka, 2012).

In August 2011, nine-axis motion-fusion algorithms (together with a triple-axis compass out-

side connected) and an upgraded version (MPU9050) of the Invensense MPU6050 were pre-

sented. After that, many manufacturers were forced to incorporate discrete motion sensor

components to deliver nine-axis Motion Interface functionality.

Sensor fusion is not limited to a nine-DOF (degree of freedom) solution. For the requirement

of indoor navigation, the 10-DOF or 10-ASF (Acclaim Skeleton File) solution includes a tri-

ple-axis accelerometer, triple-axis gyroscope, triple-axis magnetometer, and a single-axis ba-

rometer. Adding a barometer enables altitude detection, since pressure changes with altitude

at a rate of about 10 Pa/m. At the moment, some mobile motion devices or some functions of

other consumer electronics, such as iWatch, Google Glass, are based on nine-DOF or 10-DOF

sensor fusion realization.

3.6.3 Sensor Fusion Algorithms

The primary feature of IMU and AHRS is the sensor fusion. For the rotation movement

measurement with Euler angles, complex finite impulse response (FIR) or infinite impulse

response (IIR) filters such as Kalman filters, Parks-McClellan filters, are widely used. The

Direction Cosine Matrix (DCM) algorithm is used to calculate the orientation of a rigid sub-

ject, offering another way to construct a rotation matrix (Edwan et al., 2011).

In addition, most inertial manufactures present unique on-board sensor fusion algorithms. For

example, the inertial sensors from Invensens Inc. include an in-chip sensor fusion module

named Digital Motion Processor™ (DMP™), which is capable of processing the complex

nine-axis MotionFusion algorithms. ST Microelectronics Inc. presented sensor-fusion soft-

ware named iNEMO Engine, which is based on dedicated filtering and prediction algorithms.

DMP™ and iNEMO Engine combine different data from multiple sensors. These sensors can

directly provide a series of outputs such as rotation, linear acceleration, gravity, and quater-

nion. The control of these sensors can be performed using an eight-bit microcontroller (MCU)

and are independent of environmental conditions to achieve the best performance.

In addition, there are some inertial sensors embedded into a single chip with an MCU and

other function modules such as a radio frequency (RF) module.

3.6.4 Discussion

The key difference between an IMU and an AHRS is that the AHRS provides accurate atti-

tude and heading solutions (yaw angle) whereas the IMU only delivers the absolute attitude

solution (pitch and roll angles). As hand tremor is mainly a rotational movement, an AHRS is

good for measuring hand tremor. However, the magnetometer is susceptible to ferromagnetic

material, and thus needs accurate soft and hard iron calibration. As the period of hand tremor

State of the Art

27

movement assessment is short, the drift of yaw is slow and can be removed with a threshold

setting for gyroscope data. An IMU can also achieve good results when measuring hand

tremor.

The accelerometer is good at measuring linear motion and the gyroscope is good at measuring

rotational movement. When a gyroscope and an accelerometer are combined, a better result

can be realized. The combination of small, low-cost but high performance triple-axis gyro-

scopes, which have recently become available, and the existing triple-axis MEMS acceler-

ometers, enables the possibility of this six-axis measurement and control. Mobile devices

have already embraced the novel features provided by the MEMS IMU, which combines a

triple-axis gyroscope and a triple-axis accelerometer. The six-axis motion processing provides

the mobile device’s absolute position in a three dimensional space with greater accuracy, pre-

cision, and responsiveness.

3.7 Limitations of Existing Technology

The disadvantages of the state of the art are discussed in this section.

3.7.1 Real and Potentially Solvable Limitations

Subjective assessment by the surgeons and MER system are widely used to support symptom

assessments during DBS surgery. The two intraoperative approaches for neurological symp-

tom assessments show disadvantages as follows:

a) Subjective assessment by surgeons according to the five-point clinical ratings:

The judgments of the surgeons are based on their experience and differ from each oth-

er.

UPDRS and TETRAS are discrete and subjective ratings. They require a neurologist to

visually assess the patient based on experiences, and the neurologist cannot capture com-

plex symptom variations that happen in response to the stimulations during DBS surgery.

The symptom scores for the same patient may differ widely depending on the examiner

(Jankovic et al., 2007).

The coarse resolution of the ratings is insufficient for assessing small changes in tremor

severity. Furthermore, the extent of inter-clinician and inter-subject rating variability is

unknown (Machado et al., 2003).

b) Micro-electrode recording (MER):

It is an indirect motion tracking. The hair-thin microelectrodes are placed within the

intended target to record brain cell activity. The surgeon needs to inspect and listen to

the pattern of the cell activity. This physiological confirmation is also based on the

experience of the surgeon (Winestone et al., 2012).

The MER signals are spike signals, which are not good for signal processing

(Winestone et al., 2012).

Research shows that only about 67% of the cases initially planned with MER method

were taken for the final target (Bour et al., 2010).

State of the Art

28

According to the research of Bour et al. (2010), the location of the best MER activity did

not necessarily correlate with the position that produced the optimal clinical response to

microelectrode testing intraoperatively.

EMG does not directly measure body movements and a large number of electrodes may be

needed to investigate complex movements. Same with MER, the information collected on

frequency is good, but that on the magnitude is less reliable. Contact resistance is also a sig-

nificant variable. Furthermore, no information on displacement, velocity or power can be ob-

tained. The EMG waveform of tremor is spiky and exhibits an impulse chain in the morphol-

ogy. Impulse-like data are hard to analyze with Fourier spectral methods or other traditional

amplitude methods. The measured amplitude is variable and thus the result is inaccurate

(Saara et al., 2007).

Devices other than the MER described above are used for general purposes, and are not spe-

cifically designed to be used as guiding tools during DBS surgery and do not meet the needs

of the operating room. They have demonstrated limited usability in clinical settings due to

deficiencies in wearability, fidelity, and flexibility. The severities of ET and parkinsonian

symptoms, which are the effect of DBS therapy, require real-time and accurate assessment of

major parameters according to the UPDRS. The severity changes of the primary symptoms

are crucial when evaluating the effect of DBS. Easy manipulation and comfort are also im-

portant. The graphical user interfaces (GUI) of these systems are also unsuitable for the DBS

monitoring.

Some of the glove-based systems, such as Cyberglove, are accurate but relatively costly

($10,000 per Cyberglove). Others are more affordable, for example, the P5 Glove™ (ap-

proximately $100 per glove) (Dipietro et al., 2008). Most use piezoresistive sensors, fiber

optic sensors, and Hall-effect sensors, rather than inertial sensors. However, a major limita-

tion of these systems is their limited portability caused by the presence of cloth support. The

cloth support was believed to affect the measurement performance (Dipietro et al., 2008). The

data obtained from these gloves have to be modified for further processing. In addition, the

settings of the sensors are not easy to operate and calibrations are needed for new users. A

new data glove with gyroscopes and accelerometers is more feasible.

For bradykinesia assessment, finger tapping, and pronation/supination movement are not easy

to perform during DBS surgery. Rigidity is clinically defined as increased resistance to pas-

sive movement of a joint. For the joint movement, derived by a motor, has bigger dimensions

and should be fixed on a table (Patrick et al., 2001).

The displacement measurement for hand rigidity and hand grasping can be estimated through

double integration of raw acceleration data over time. However, gravity vector and bias

should be deducted from the raw acceleration. Then IMUs, which includes three-axis gyro-

scopes, should be utilized in this study.

3.7.2 Limitations That Cannot Be Solved at This Time with Reasonable Effort

The basic changes in the EMG and MER signals, which are caused by PD or ET, are an in-

creased tonic background activity and an alternating pattern of signal bursts. During the EMG

or MER analysis of the symptoms of PD and ET, special attention has been paid to the analy-

sis of these bursts by measuring their counts, magnitudes, durations, and frequencies

(Rissanen et al., 2007). For the measurement of vigorous movement, an IMU provides good

State of the Art

29

results. However, for slight motion disorders, the difference between EMG and IMU requires

further validation.

Tremor, bradykinesia, and limb rigidity are the characteristic features of PD. However,

bradykinesia responds to DBS after a period of hours, whereas tremor and rigidity respond

within seconds (Prodoehl et al., 2007). For the design of the assessment tasks, there is no con-

cern about this situation yet.

The UPDRS is a subjective rating and the rigidity scores for the same patient may differ wide-

ly depending on the examiner. Because of the role of the patient’s passive movement in as-

sessing rigidity, the performance of the examiner also affects the assessment results through a

rigidity assessment system without a motor (Patrick et al., 2001).

Rigidity occurring in PD patients commonly has a “cogwheel” character, which is not repre-

sented by the UPDRS (Van Dillen et al., 1988).

The extent of hand tremors in PD varies between patients. The ratio of rotational motion to

linear motion is not fixed. An optimal ratio that fits all patients can only be obtained after a

large number of clinical measurements have been taken (Timmer et al., 1993).

The goal of DBS surgery is to obtain the best treatment strategy, because PD affects every

patient differently. Tremors and other symptoms do not appear at the same time for every

patient. The assessment results of a patient with the designed system also depend on the skill

level or psychological factors of the patient. The significant individualized factors of the pa-

tient include the age, years of disease, level of functional impairment, concurrent medical

issues, and sensitivity to medications (Spieker et al., 1995). Therefore, it is important to con-

sider various individualized factors of the patient when utilizing assessment results. These

factors can be investigated only after long-term use of the glove monitoring system. Objective

assessment methods which fit for all patients with PD or ET are needed in the assessment of

their symptoms.

Glove Monitoring System

30

4. Glove Monitoring System

Because all the primary symptoms of PD and ET can be assessed on the hand, a glove moni-

toring system based on inertial sensors and force sensors is supposed to monitor the severities

of tremor syndromes, bradykinesia, and rigidity during DBS surgery.

Chapter 4.1 introduces the task description of the glove monitoring system. Several assess-

ment tasks for tremor syndromes, bradykinesia, and rigidity were chosen for the glove moni-

toring system according to the requirements of DBS surgery. The supposed parameters and

their accuracy settings, together with the other requirements, are presented in this section too.

The expected advantages of the glove monitoring system are described in Chapter 4.2.

4.1 Task Description

Currently there is no system available for assessing all the primary PD symptoms. Therefore,

it is important to realize three assessment tasks in one system. The goal of this project is to

develop a measuring system to be used in DBS surgery, which quantifies the severity of hand

tremors, bradykinesia, and rigidity.

4.1.1 Assessment Movements in This Study

According to the literature and the requirements of neurosurgeons, several assessment tasks

should be chosen to assess the severity of the symptoms of PD and ET. All the assessment

tasks are supposed to have the same duration (from five seconds to one minute). The surgeon

instructs the patient to perform different tasks. The observers can view all the parameters and

raw signals on the computer.

According to the MDS-UPDRS, tremor assessments include three tasks to test for the rest

tremor, postural tremor, and action tremor.

According to the requirement of surgeons, whole-hand grasping was chosen as the

bradykinesia assessment task because the patients can perform it easily during DBS surgery.

Figure 4-1: Rigidity assessment task. 1) subject; 2) rigidity cuff; 3) examiner. Here l is the arm length of

the patient. The examiner flexes and stretches the elbow through the rigidity assessment cuff attached to

the wrist. Several cycles are performed during the 10-second assessment task. The examiner should make

sure the elbow position of the patient is stable (Based on Dai et al., 2013b).

Passive flexion and extension of the elbow was used to assess rigidity. Figure 4-1 shows the

rigidity cuff and rigidity quantification task in this project. The rigidity cuff is strapped to the

Glove Monitoring System

31

distal end of the patient’s forearm. An examiner flexes and extends the patient’s elbow joint

through force at the point of the rigidity cuff on the wrist.

After discussion with the surgeons, the duration of a single task in this study was set at ten

seconds. Each time two or more measurements for a single task should be repeatedly per-

formed for better results.

4.1.2 System Objectives and Parameters

The parameters that should be obtained from both time- and frequency-domain signal proc-

essing methods and displayed in the GUI are listed as follows:

Severity of hand tremors (frequency and amplitude of tremors).

Severity of finger bradykinesia (mean and standard deviation values of angular dis-

placements in hand grasps; dominant frequency in hand grasps).

Severity of elbow rigidity (viscosity, elasticity, and the dominant frequency of elbow

movement).

The angular displacements in the bradykinesia task represent peak-to-peak values of the hand

grasping ranges during one time bradykinesia task.

In addition, the power spectral density of the signals from the gyroscope and accelerometer

(from 0.25 Hz to 12 Hz), together with the raw data, needs to be displayed in real-time.

The severities of tremors, bradykinesia, and rigidity should be displayed at levels from 0 to 4

according to the UPDRS ratings. There is, however, no literature about the bradykinesia pa-

rameters of hand grasping movement and UPDRS scores. The severity of parkinsonian rigid-

ity is very difficult to assess. Therefore, the severity scores of bradykinesia and rigidity ac-

cording to the present parameters need to be further investigated.

Fluctuations in the motor performance (ON/OFF fluctuations) of PD patients can also be indi-

cated by the changes in these parameters.

For DBS electrode positioning, only the tremor amplitude is required, because the tremor fre-

quency has no relation to the symptom severity of parkinsonian tremor or ET. The tremor

amplitudes, which are regressed from the peak powers of the IMU signals, can be gauged as

the clinician ratings. However, the parameters of bradykinesia and rigidity cannot be normal-

ized directly as UPDRS ratings at present.

Unlike for tremor, peak power during hand grasping is not correlated with the clinical

UPDRS score. Instead, the mean value and standard deviation (SD) of hand grasping ranges (

and ), where is the peak-to-peak values of the hand grasping cycles, can be used as

the parameters of bradykinesia. However, the correlation between these two values and the

UPDRS score needs further study (Post et al., 2005).

In the clinic, rigidity is assessed by a neurologist who moves the subject’s limb, scoring the

result according to the UPDRS ratings. However, rigidity scores for an individual patient may

vary depending on the examiner. Such scales are susceptible to the problems of sensitivity

and reliability. Mechanical impedance, which means the magnitude of the vector sum of elas-

Glove Monitoring System

32

tic stiffness and viscous stiffness, is nonlinearly related to UPDRS rigidity ratings (Charles,

2003).

4.1.3 Accuracy Settings of the Parameters

After the clinical measurements and experiments, the correlations between the measured pa-

rameters (dominant frequency and amplitude in each tremor task; dominant frequency, mean,

and standard deviation values of grasping ranges in bradykinesia task; viscosity, elasticity,

and frequency of elbow movement) and the UPDRS or TETRAS scores can be determined.

For calculating the accuracy settings in Table 4-1, the parameters obtained from the glove

monitoring systems with the IMU and force sensors must be compared to the judgments of

doctors according to the UPDRS ratings. The parameters should meet the accuracy setting as

shown in Table 4-1. In Table 4-1, is the coefficient of determination and RMSE is the root-

mean-square-error (RMSE).

Table 4-1: Parameters and their required accuracies. For the RMSE in rigidity parameter (mechanical

impedance), there is no relative literature.

Task Parameter Unit,

[Range]

Required accuracy Reference

2r RMSE

Rest tremor R1 1, [0–4] 0.85 0.32 Giuffrida et al.,

2009

Postural tremor R2 1, [0–4] 0.88 0.32 Giuffrida et al.,

2009

Action tremor R3 1, [0–4] 0.60 0.45 Giuffrida et al.,

2009

Bradykinesia °, [0–360] 0.72 0.52 Heldman et al.,

2011

Bradykinesia °, [0–360] 0.62 0.65 Heldman et al.,

2011

Rigidity Z N/°, [0–50] 0.35 - Patrick et al.,

2001

With patients and volunteers, the functionality and accuracy of the glove monitoring system

should be tested and verified.

4.1.4 Requirements

General requirements of the glove monitoring system are listed as follows:

Easy to perform for both the surgeons and patients (based on the intraoperative test

tasks).

Help the surgeons to quantify symptom severities during DBS surgery and as feedback

to the electrode stimulation.

Accurate and reliable: the parameters should meet the accuracy settings in Table 4-1.

Quantify symptom severities according to the UPDRS or TETRAS ratings.

Provide parameter-recording lists to compare symptom severities in all tested elec-

trode positions.

Glove Monitoring System

33

High security and low risk.

Does not restrict the patient’s movement (comfortable).

Portable and with small dimensions.

Meet the requirements of the operating room.

Some technical requirements are listed as follows:

Accurate calibration plays a key role in the IMU measurements: the accuracy of the

force measurement and the angular displacement measurement should be higher than

20% and 10% respectively.

A large amount of clinical measurements is needed for the modification of algorithms,

especially the coefficients of the regression models used in this project: 30 patients for

each symptom should be measured with this glove monitoring system.

The raw data from the sensors should be displayed to show the original hand move-

ment. The raw data and measured parameters should also be stored in a file. A real-

time PSD display of the inertial sensor signals over the entire frequency range (0.25–

10 Hz) should be incorporated.

The user graphical interface should be easy to use and intuitive, both for technicians

and surgeons.

A database with customized reporting functions and data management is required.

The assessed parameters should be updated quickly when the assessed symptom

changes. These parameters should be saved and displayed for contrast when the elec-

trode is moved to different positions.

To avoid radio frequency interference (RFI) in the operating room, a wired measure-

ment device is required during DBS surgery. A wireless measurement device is pre-

sented only for the measurement outside the operation room.

4.2 Expected Advantages

The major advantages of this glove monitoring system are:

Objective assessment, thus easing the surgeons’ workload.

This project focuses on DBS, specifically integrating the overall measurement of finger and

elbow movement. By incorporating a glove for measuring and displaying the parameters and

waveforms on a computer, the system will be easy to use and relieve the surgeon. The PSD

method in this project does not simply divide the time dimension into windows (3–5 seconds

or longer), and a single data point can be used more than once. Thus the PSD chart is continu-

ous with the sampling interval. The single-sided, scaled, auto-power spectrum of the time-

domain signals will be displayed together with the raw signals. This feature will allow better

detection of changes of the symptoms.

Three primary symptoms of PD and ET are implemented in a portable system.

Glove Monitoring System

34

Unlike previous studies, the three primary symptoms are included in a system. In addition,

this system meets the requirements of DBS surgery.

Electronics safety because the electronic parts have no contact with the patient’s

body.

These sensors have no direct contact with the human body, thus this system is safer than

EMG and MER.

MEMS IMU technology makes the system smaller but with a higher performance

compared to previous motor assessment systems.

With ever-smaller dimensions and higher performance, it is easy to detect motor disorders in

PD and ET. The latest motion MEMS sensors make it is possible to realize motor disorders

with small dimension and higher performance.

Higher resolution compared to the UPDRS or TETRAS ratings: 0.01.

Each sub-scale of the UPDRS are from 0 to 4, where 0=normal, 1=slight, 2=mild,

3=moderate, and 4=severe. Most of the previous research only compared the outputs of their

systems to this five-point rating scale (Elble et al., 2006). During DBS, a higher resolution for

all parameters is needed.

Accelerometer data showed strong correlations with UPDRS rest tremor scores for both trem-

or duration and amplitude, especially when the hand tremor involved non-rotational move-

ment. However, accelerometers measure linear acceleration and are influenced by gravity,

whereas gyroscopes measure gravity-independent angular velocity. Tremor measured using

gyroscopes correlated well with UPRDS scores, with higher sensitivity and specificity than

previous studies that used accelerometers (Giuffrida et al., 2009). By using a six-axis sensor

motion fusion method, the measured parameters are more sensitive to hand movements in all

directions.

After surgery, this glove monitoring system also can be used to track the progress of the pa-

tients and to provide feedback to physicians on the long-term results of surgery. When the

patient returns for periodic readjustment of the neurostimulator, the glove monitoring system

can be used to provide quantitative measurements of tremors and bradykinesia to assist in

selecting the optimum parameters for electrode stimulation.

System Concept

35

5. System Concept

Two designs using wireless communication interfaces are first introduced in Chapter 5.1.

These designs could be used for a series of tremor and bradykinesia assessment tasks. All

fingers were attached with sensors. These designs are not for applications outside of the oper-

ation room, other than for DBS surgery.

According to the requirements during DBS surgery, the static and dynamic system descrip-

tions of the glove monitoring system are described in Chapter 5.2 and Chapter 5.3 respective-

ly. The first step in designing the glove monitoring system includes two parts: one for tremor

and bradykinesia assessment, the other for the rigidity assessment. In the end, a combined

version based on the two separate systems is introduced.

5.1 Designs with Wireless Communication Interfaces

At the beginning of the study, there were two designs for PD assessment based on wireless

communication, touch sensors, and inertial sensors (gyroscope and accelerometer). Several

prototypes based on these designs have been carried out but they were not for the purpose of

DBS. They could be used for the parameter settings of the neurostimulator after DBS surgery.

According to the literature, accelerometers and gyroscopes placed on each finger can collect

enough information about parkinsonian tremors (Dipietro et al., 2008). A gyroscope is better

for bradykinesia assessment. This suggests that a triple-axis accelerometer and a triple-axis

gyroscope on each finger will provide sufficient information regarding the hand activity of

patients with PD or ET. Compared to a gyroscope and an accelerometer on one finger, the use

of six gyroscopes and six accelerometers on all fingers and the wrist would provide more in-

formation about hand movement. Thus, the quantification of tremors and bradykinesia would

be more objective.

Figure 5-1 shows the diagram of the first tremor and bradykinesia assessment system with a

wireless interface. In addition to inertial sensors, four touch sensors were attached to the fin-

gers as well. The touch sensors were used for bradykinesia assessment (finger tapping task).

Figure 5-1: Components of the tremor and bradykinesia measuring system with wireless communications.

The mean tremor amplitude in all fingers, distance, and duration between two finger taps were

calculated as the parameters of tremor and bradykinesia, respectively. As a result, from the

measurement of the difference between finger motions, the obtained parameters could be

more accurate.

Sensor nodes (IMUs and touch sensor positive points)

Command module with wireless interface

Wireless receiver

Sensor node (IMU and touch sensor ground point)

System Concept

36

After the analysis of these parameters, fewer sensors were adopted but with the same quality.

For a simplified version of this design, the thumb and forefinger should be placed with the

inertial sensors and touch sensors.

During DBS surgery, the glove monitoring system is wired to a computer, whereas after sur-

gery a wireless system can be used. This system was for the assessment outside of the opera-

tion room and could not assess rigidity severity.

5.2 Static System Description

Figure 5-2 shows the original system diagram of the glove monitoring system. The glove

monitoring system is based on MEMS IMUs, FSRs, and a medical textile glove. A sensor

board with an IMU placed in the upper side of a finger is used to assess tremors and

bradykinesia. The rigidity assessment part consists of a rigidity cuff, which was able to be

attached to the wrist and includes two differential force sensor boxes and another IMU. The

rigidity assessment cuff is designed to attain the model of a joint’s movement state (angular

displacement and velocity) and its measured torque (N•m), which includes non-neural torque

and neural torque. The sensor data is acquired by a command module and sent to a computer

via a USB cable. The sensor board, command module, and rigidity cuff can be integrated into

a textile glove. Further signal processing is carried out on a computer using MATLAB, Visual

Studio, or LabVIEW.

a) b)

Figure 5-2: a) general system diagram of the glove monitoring system; b) rigidity assessment cuff. The

components are: 1) sensor board (IMU); 2) textile glove; 3) command module; 4) rigidity assessment cuff;

5) USB cable; 6) graphical user interface (Based on Dai et al., 2013a).

The computer communicates with the command module via a USB cable (Lorenzo, et al.,

2011). The wired communication, instead of wireless communication, has the advantage that

this system even can be used in the operation room.

A 6-axis IMU module (combines a triple-axis gyroscope and a triple-axis accelerometer) on

the upper side of the finger is connected to the command module using a Two-Wire Interface

(TWI). The IMU module is used for tremor and bradykinesia assessments.

An IMU is attached to the wrist while two force sensor boxes are on both sides of the wrist.

These sensors are used in the rigidity assessment. A medical textile glove incorporates the

command module and the sensor board (IMU). A series of textile gloves are used to fix the

command module and the sensor board to the user’s hand.

The positions of all sensors are shown in Figure 5-3.

System Concept

37

Figure 5-3: Positions of the sensors. For the position of the sensor board (IMU), only one finger from the

index finger and middle finger was chosen according to tremor or bradykinesia tasks.

Figure 5-4: System diagram for an IMU chip. Two Wire Serial Interface (TWI) is compatible with the

Philips’s IIC protocol.

The system diagram of an IMU chip is shown in Figure 5-4. It includes a gyroscope and an

accelerometer.

Figure 5-5: Two parts of the glove monitoring system.

At first, two separate parts were implemented as Figure 5-5 shows, because rigidity assess-

ment is different from tremor and bradykinesia assessment. One part was the system for the

tremor and bradykinesia assessment, while the other was for the rigidity assessment.

5.2.1 System for Tremors and Bradykinesia Assessment

Parkinsonian tremor is called “pill-rolling” tremor, as it is likened to rolling a pill between the

thumb and index finger, because of this, the IMU was placed on the upper side of the index

finger (Giuffrida et al., 2009). For bradykinesia assessment, the inertial sensor in the middle

finger has the best effect. All the data were transmitted to the computer in real-time via a seri-

X-axis

Gyroscope

Y-axis

Z-axis

ADC

ADC

ADCR

eg

iste

rs

X-axis

Accelerometer

Y-axis

Z-axis

ADC

ADC

ADC

Inte

rface

Re

gis

ters

2-wire busMCU

Commnd moduleand sensor board

Computer

Data

USB

Rigidity cuff

Data

Glove

Rigidity assessment part

Tremor/bradykinesia assessment part

System Concept

38

al-to-USB communication interface. The system diagram of the tremor and bradykinesia

quantification system is shown in Figure 5-6.

Figure 5-6: System diagram of the tremor and bradykinesia quantification system. 1) sensor board; 2)

command module; 3) textile glove; 4) universal serial bus (USB) cable; 5) GUI.

5.2.2 System for Rigidity Assessment

The level of parkinsonian rigidity was able to be measured using an IMU and force sensors on

the wrist. Figure 5-7 shows the system diagram of the rigidity assessment system. The rigidity

cuff was connected to the computer via a USB cable.

Figure 5-7: System diagram of the rigidity assessment system (Based on Dai et al., 2013b).

Because the movement of the wrist and elbow has two directions: passive (PA) and contrala-

teral active (CA), both sides of the wrist need a force sensor box.

Each force sensor box included four force sensitive resistors (FSR sensors or FSRs), which

were in parallel connection to one output. The output connected one end to the power supply

and the other to a pull-down resistor to the ground. The point between the fixed pull-down

resistor and the force sensor box was connected to the analog input of a microcontroller.

Compared to a single force sensor, the force sensor box had the benefits of higher measure-

ment stability and a bigger contact patch for the examiner. Two force sensor boxes were con-

nected to the command module. The IMU part (a triple-axis gyroscope and a triple-axis accel-

erometer) was also included in the command module. All the data were transmitted to the

computer via a serial-to-USB communication interface.

Figure 5-8 shows the structure of a force sensor box. Four FSR sensors were located in the

four bottom corners of the housing and were in contact with the rubber feet on its upper side.

This structure has the advantage that the force sensor box provides almost the same value

when an examiner presses on every point of the housing’s upper side with the same force. The

Tremor score:

Bradykinesia score:

ComputerGlove

5

4

2

3

1

1

System Concept

39

viscosity and elasticity of the elbow, which are the major components of mechanical imped-

ance, were calculated with the sensor data and displayed in the GUI (Post et al., 2009).

Figure 5-8: Structure of the force sensor box (Based on Dai et al., 2013a). A force sensor box consisted of

four FSRs in parallel configuration. These four FSRs were located on each corner of the box

Elastic stiffness depends on the torque and angular displacement of the elbow movement (Pat-

rick et al., 2001). If a joint shows viscous behavior, it means that the measured torque de-

pends on movement velocity. In order to avoid modeling the viscous component, some re-

search groups chose to either maintain a constant velocity by using motor actuated systems, or

to advise examiners to impose the same movement on all subjects. This study does not utilize

a motor part in order to keep the device portable nor does it change the clinical assessment

course.

5.2.3 Combined Version

Because the rigidity assessment system was separated from the tremor and bradykinesia as-

sessment system, a new concept with all three symptom assessments is presented. Figure 5-9

shows the system diagram of the combined glove monitoring system which can assess all

primary symptoms. The circuit board and force sensors are embedded into a cuff which is

produced by a 3D printer. There is only a microcontroller in the glove part. The textile glove

is no longer necessary.

Figure 5-9: System diagram of the combined system. All the sensors are connected to a single microcon-

troller. The command module and force sensor boxes are embedded into a case.

Figure 5-10 shows the GUI diagram of the combined system. There are three major parts:

recording lists in the upside of the GUI, start buttons for each task, and progress bars for each

task.

The assessment tasks and algorithms of the separated systems and combined system are still

the same.

System Concept

40

Figure 5-10: GUI diagram of the combined system. The start buttons, progress bars, and recording lists

for each assessment task are listed in the GUI at the same time. Thus it is better for the surgeon to com-

pare the parameters.

5.3 Dynamic System Description

Figure 5-11: Flowchart of the signal processing in the glove monitoring system (Taken from Dai et al.,

2013a). The glove part was based on the sensors and command modules. The computer part was based on

the program in the computer. The communication between them was the serial-to-USB port.

The flowchart of the signal processing methods in the glove monitoring system is shown in

Figure 5-11. The system comprised of two major components: the glove part and the comput-

er part. The sensor data were collected via a microcontroller. The microcontroller sent data to

the computer at a defined time interval.

Timer interruptInitiation

InitiationReceive

sensor data

Dequeue dataData pre-processing

Tremor/Bradykinesia/Rigidity

quantification

Display and

save results

Acquire and send sensor data

Enqueue

data

Glove Part

Computer Part

System Concept

41

The sensor reading was taken by the microcontroller at timed intervals. Gyroscope and accel-

erometer data were simultaneously sampled in order to get high-quality position coordinate

information. Low-pass filtering (LPF) was employed in all sensor outputs for the reason of

anti-aliasing measures.

Inside the glove module, the sensor data were obtained and sent to the computer via a USB

interface. Inside the computer, the received data were stored in a queue. The last cycle’s data

was de-queued for analysis at the same time. Three signal processing methods realized the

three assessment tasks correspondingly. After each assessment task, the parameters according

to the UPDRS score were listed in the recording list. The maximum and minimum values of

the parameters were kept at the bottom of the recording list automatically.

Table 5-1 shows the quantitative models for the objective quantification of neuromotor symp-

toms. These models are based on estimation theory or other statistical analyses.

Table 5-1: Quantification algorithms of the neuromotor symptoms (PD and ET)

TASK PARAMETERS AND METHODS

Tremors Amplitude (Linear regression model)

Bradykinesia Mean and standard deviation of grasp ranges (Statistic analysis)

Rigidity Elasticity and viscosity (Least-squares parameter estimation)

For the rigidity task, viscous and elastic components should be calculated before the calcula-

tion of mechanical impedance.

The computer received the data for continuous signal processing, storage, and display. Data

input and analysis took place in a multi-thread mode, with the use of a queue.

5.3.1 Processing Methods of Tremor Amplitude and Frequency

The algorithms used to quantify the severity of tremors are very important. Some researchers

have proposed objective methods to detect and quantify the tremor severity.

According to the state of the art, Fourier-based spectral analysis, statistical measure (quadratic

mean), and other computational methods such as neural network or fuzzy classifier are per-

formed with the inertial sensor data for the tremor amplitude quantification (Burkhard et al.,

2002; Narcisa et al., 2011; Patel et al., 2009). However, spectral analysis is used for the ma-

jority of these studies.

In this study, signal processing involves IIR and FIR filters as well as other special algorithms

such as PSD analysis. To detect tremors, the signals are then processed with auto power spec-

trum with a certain time length (3–10 seconds).

PSD Estimation

As Figure 5-12 shows, for PSD estimation, peak power means the power estimation around

the dominant frequency in the power spectrum of sensor signals.

System Concept

42

Figure 5-12: PSD estimation of ten-second inertial sensor signals. fdominant represents the dominant fre-

quency of the signals.

Equation 5-1 shows the calculation of the peak power of inertial sensor signals:

, (5-1)

where * denotes the complex conjugate and N is the number of sample points in the sensor

signals.

The formula of the one-dimensional FFT in Equation 5-1 is described as

, (n=0, 1, 2, …, N-1), (5-2)

where X is the input sequence, N is the number of elements of X, and Y is the transform result.

The frequency resolution is fsample rate/N, while fsample rate is the sampling frequency.

Then the power spectrum is converted into a single-side power spectrum. The power spectrum

magnitude (peak power) has units of the input signal unit-RMS squared. The power estima-

tion units of IMU signals are (°/s)2

/Hz and g2/Hz, respectively. Here g is equal to m/s

2.

Tremor Amplitude Estimation

For range (displacement) analysis, the angular velocity obtained from the gyroscope needs to

be integrated over time only once, but the integration of the acceleration signals requires dou-

ble integration. However, the sensor bias and drifts are integrated as well. Thus, the raw data

from the inertial sensors are used for the tremor quantification.

According to the research conducted by Giuffrida et al. (2009), for the rest tremor and postur-

al tremor in PD, the logarithm of the peak powers’ summation of both power spectrums of

accelerometer and gyroscope data had the highest correlation with UPDRS scores (coefficient

of determination r2=0.9). For the action tremor in PD, the RMS sum of both gyroscope and

accelerometer data had the highest correlation with UPDRS (r2=0.69) (Heldman et al., 2011).

In this system, the logarithm of the peak powers’ summation of the IMU sensor signals is

regarded as the tremor amplitude.

Given its oscillatory nature, tremors are well suited to spectral analysis, the most popular

method for tremor quantification (Cameron et al., 1997). The goal of PSD is to describe the

0 f1

f2

fdominant

Frequency (Hz)

Po

we

r e

stim

atio

nPeak power

System Concept

43

power distribution of a signal based on a finite set of data over the frequency domain.

Quadratic mean (RMS) interprets actual levels, while PSD results show frequencies that con-

tribute the most to the tremor. Because the tremors are based on a dominant frequency, the

advantages of PSD compared to a statistical measure (quadratic mean) are that it highlights

the tremor signals from noise and other movement with analysis in the frequency dimension

and the squared value of the signals. There are nonparametric methods, parametric methods,

and subspace methods for PSD estimation. The nonparametric methods include periodogram,

Welch's method, and the multitaper method (MTM). Parametric methods are used to estimate

the output signal from a linear system driven by white noise. Subspace methods, which are

also known as high-resolution methods, are used to generate frequency component estima-

tions for the signal based on an eigenanalysis or eigendecomposition of the correlation matrix.

The tremor signals will be tested with different types of methods for PSD estimation at a later

time. In addition, for the offline tremor data analysis, PSD results should be used together

with quadratic mean results to identify the overall features of tremors.

The flowchart of the signal processing for the tremor quantification in this study is shown in

Figure 5-13. The sensor data are from the glove part, which is based on the sensor board and

command module. In the computer part, the sensor data are band-pass filtered from 3 to 12 Hz

(tremor band) before tremor amplitude quantification.

Figure 5-13: Signal processing for the tremor detection and quantification in a single finger (four chan-

nels).

Accelerometer X, Y,Z Signal

Drift Cancellation

RMS Calculation and

Remove Gravity Component

Auto Power Spectrum(RMS)

Power & Frequency Estimation

Power Peak & Dominate

Frequency (Pa;Fa)

Gyroscope X,Y,Z Signal

Drift Cancellation(X,Y,Z)

Band-pass Filter(X,Y,Z)

Auto Power Spectrum(X,Y,Z)

Power & Frequency EstimatIon

(X,Y,Z)

Power Peak & Dominate

Frequency

(Pgx,Pgy,Pgz;Fgx,Fgy,Fgz)

P=Max(Pa,Pgx,Pgy,Pgz)

Dominate Frequency F=0 P>0.05

F=Fa

F=Fgx

F=Fgy

F=Fgz

Estimated Power: P' around FDominate Pole: P', F

*Scaling Factor

*Scaling Factor

Band-pass Filter

P=Pa

N Y P=Pgx

P=Pgy

P=Pgz

Y

Y

Y

Y

System Concept

44

Initially, the signals from the gyroscope and the accelerometer in a single finger are analyzed

separately. The single-sided, scaled, and auto power spectra of the signal of each gyroscope

axis and the combined values of all three axis outputs of the accelerometer, of which length is

set to three seconds, are separately computed to get the four-channel power spectra in real-

time. The dominant frequencies and the estimated peak powers of these frequencies are thus

obtained. Then the estimated peak powers will be multiplied by scaling factors. The channel

with the highest peak power is the dominant channel, and its dominant frequency is the domi-

nant frequency (F) of all channels. Other channels are needed to perform PSD estimations

again with the dominant frequency, and multiply the peak powers by the scaling factors again.

The power estimations in all axes around the same dominant frequency (F) are calculated and

sum up to P'.

The power spectrum of every axis in a certain period (three seconds) is calculated continuous-

ly. The dominant frequency and peak power can be obtained as soon as the tremor occurs.

When the dominant frequency is between 3.5 Hz and 7.5 Hz and the sum of peak powers is

more than a threshold, the tremor is reported.

However, the signals in ten seconds are used for the PSD estimation at the end of the ten-

second tremor assessment task. Then, the sum of peak powers, which is regarded as the trem-

or amplitude, and the dominant frequency can be displayed in the recoding list.

PSD estimation is the most popular method for tremor amplitude calculation, because the

tremor is a rhythmic and involuntary movement based on a dominant frequency (Patel et al.,

2009). Gyroscope and accelerometer react respectively to rotational and linear movements.

Then a linear regression model is used to fit the clinical ratings (UPDRS tremor scores and

TETRAS) and the peak powers from both the gyroscope and accelerometer signals.

The total output of a triple-axis accelerometer can be expressed as axyz:

222

zyxxyz aaaa . (5-3)

axyz also includes gravitational acceleration, which equals 9.81 m/s2

in vector product. Gravita-

tional acceleration can be removed from the accelerometer outputs with high-pass filters.

Then there is only one axis acceleration data for the following signal processing.

The dominant frequency can be calculated using PSD estimation. If the dominant frequencies

in different axes are not the same, the valid dominant frequency in the axis with the highest

peak power is defined as the dominant frequency of all axes. Then the total peak power in all

four axes, which includes axyz and three-axis gyroscope signals, is the power of all axes’ data

around the valid dominant frequency with the PSD method. The peak power in all axes after

normalization is regarded as the amplitude of tremor. Heldman et al. presented the discovery

that the logarithm of the peak power in all triple-axis accelerations and three-axis angular ve-

locities correlates well with the clinical scores of tremor (Burkhard et al., 2002).

Because the accelerometer and gyroscope are used to measure linear and rotational movement

respectively, the accelerometer presents a higher correlation for some tremor tasks, while the

gyroscope performs a higher correlation for other tremor tasks. Then a multiple linear regres-

sion model is used to fit the clinician ratings (UPDRS tremor scores) and the peak powers

during each tremor task (Timmer et al., 1997):

Then the linear regression model (Giuffrida, et al., 2009) can be expressed as

System Concept

45

R = R0+ ln (b0·PAxyz+cx·PGx+cy·PGy+cz·PGz), (5-4)

where R is the predicated tremor score; R0, b0, cx, cy, and cz are the regression coefficients;

PAxyz, PGx, PGy, and PGz are the peak powers for the triple-axis accelerometer and triple-axis

gyroscope, respectively. R0 and the three scaling factors are different for the three tremors

(rest, posture, and action).

For different tasks and different tremor types, these regression coefficients are different. The

peak power in this study is the power estimation around the dominant frequency with ± 0.3

Hz length in the single sided power spectrum of ten-second sensor signals (Heldman et al.,

2011).

These three regression coefficients were obtained according to Stevens’ power law in psycho-

physics (Luce & Krumhansl, 1988). Table 5-2 shows the initial coefficients and scaling fac-

tors for different tremor tasks.

Table 5-2: Coefficients and scaling factors of the tremor amplitude regression models

Tremor Coefficients

R0 b0 cx cy cz

Rest 0.8 10 0.001 0.001 0.001

Posture 0.6 5 0.0001 0.0001 0.0001

Action 0.3 2 2e–5 2e–5 2e–5

In the future, the parameters in Table 5-2 will be modified according to the results of meas-

urements in patients with tremor.

The occurrence of tremor in a patient depends on many factors. Tremor can disappear some-

times even for a patient with severe tremor. Therefore, it is important to quantify tremor se-

verity during the stable tremor state. The tremor state is classified into two types in this pro-

ject: valid state and invalid state. The signals in invalid state will be discarded.

After a ten-second tremor assessment task, the tremor signals need to be checked. Valid state

means the stable state in both the time domain and frequency domain. The judgments of valid

state are listed as follows:

In the frequency domain of ten-second signals, the proportion of peak power to the

whole power estimation should be bigger than 85%.

In the time domain, the standard deviation of ten-second angular velocity ranges

(peak-to-peak values of all axes of the gyroscope) should be smaller than 30% of the

mean gyroscope signal ranges.

5.3.2 Processing Methods of Bradykinesia Parameters

Hand grasping is easier to perform during DBS surgery. The signals of healthy people have a

consistent amplitude and frequency, thus appearing sinusoidal. On the other hand, patients

with severe bradykinesia have an inconsistent amplitude and frequency.

System Concept

46

Signal processing for bradykinesia quantification is done by the computer. After receiving the

sensor data from the microcontroller, the gyroscope signals are filtered firstly in real-time.

The flowchart of bradykinesia detection and quantification using a gyroscope is shown in

Figure 5-14.

As shown in Figure 5-14, the angular displacement during hand grasping can be calculated by

numerical integration of the triple-axis angular velocities at the end of the middle finger. Then

the mean value and standard deviation value ( and ) of hand grasping ranges ( ) can

be acquired by statistical methods. After a ten-second assessment, a second-order integration

is performed with all the gyroscope signals. Then a peak-detection algorithm and statistical

analysis are performed.

Figure 5-14: Flowchart of the signal processing for bradykinesia quantification in the computer (Based on

Dai et al., 2013a). Here peak-to-peak angles, which are calculated with a peak detection algorithm, mean

the peak to peak values of all hand grasping cycles.

Figure 5-15 shows the peak-detection method during bradykinesia quantification.

Figure 5-15: Peak detection for grasping ranges in a bradykinesia task. There are five hand grasping

cycles in this figure. A peak-detection algorithm could be used to calculate peak to peak angle values

(Ranges 1 to 5: 1 to 5). An angular displacement threshold, for example ±20º for both maximum

and minimum, can be used to remove the unnecessary peak points.

The grasping ranges ( PPα or ) are the three-dimensional peak-to-peak values during grasp-

ing cycles of a bradykinesia task. At first, the triple-axis grasping ranges are calculated sepa-

System Concept

47

rately. The combined triple-axis grasping range ( ) is the sum of the three-axis grasping

ranges ( ).

The number of peak to peak angle values during the ten-second assessment period is related

to the dominant frequency of hand grasps. The mean value of peak to peak angle values

(grasp ranges) represents the amplitude of bradykinesia. The standard deviation value of grasp

ranges represents the change of amplitude during the grasping task.

The mean and standard deviation of hand grasping ranges are easy to calculate with statistical

methods. The dominant frequency of hand grasps is calculated from the gyroscope signals

using the FFT method.

The observed parameters are:

Dominant frequency of hand grasping movement (f).

Mean range in hand grasps ( ).

Standard deviation value of hand grasp ranges ( ).

These parameters are calculated based on the gyroscope signals from the middle finger. For a

patient with mild bradykinesia, the signals obtained from the gyroscope should have a con-

sistent amplitude and frequency and should appear sinusoidal. Conversely, the signals from a

patient with severe bradykinesia should have a much lower and inconsistent amplitude and

frequency. Thus the mean value and SD value of the hand grasping ranges and hand grasping

frequency during a bradykinesia assessment task represent the bradykinesia severity (Zhang et

al., 2011). The grasp cycles during a bradykinesia assessment task (ten seconds) are approxi-

mately equal to ten times the dominant frequency of the hand grasping movement (f).

Equation 5-5 and Equation 5-6 show the mathematical formulas used to determine and

:

, (5-5)

, (5-6)

where is the combined hand grasping range (peak-to-peak values) in a single grasp cycle,

and N is the number of the hand grasping cycles in a ten-second bradykinesia task.

Grasping Angle Calculation

There are three methods for the hand grasping angular displacement (grasping range) calcula-

tion:

Triple-axis tilt angle calculation based on the gravity accelerations from a triple-axis

accelerometer.

Triple-axis angular velocity integration calculation with the triple-axis gyroscope sig-

nals.

System Concept

48

Six-axis sensor fusion with the signals from a triple-axis gyroscope and triple-axis ac-

celerometer.

Tilt calculation is a static measurement. The gravity is used as an input to determine the orien-

tation of an object calculating the tilt degree.

For the angle calculation with the triple-axis gyroscope signals, first order approximation

(trapezoidal method), Simpson’s rule or bodes rule can be utilized.

For sensor fusion, the gyroscope is used as the primary source of orientation information. The

accelerometer is used for roll-pitch drift correction.

It is not easy to separate the gravity components from the accelerometer data as the hand

grasping is not static movement. A Kalman filter or the DCM algorithm is used to combine

the data from the gyroscopes and accelerometers to produce an output that is better than that

obtained from individual sensors. However, sensor fusion (DCM or Kalman filter algorithm)

makes the program more complicated, because several parameters need to be set and there are

several seconds of adaptive time before the stable state is reached in the program (Madgwick

et al., 2011).

Second-order integration approximation (Simpson’s method) is easy to perform, and the drift

in ten seconds has a small effect on the angle calculation. Then the grasping range is calculat-

ed via triple-axis angular velocity integration and combination (National Instrument Inc.,

2011). The single-axis angular displacement (α i) in i-th sampled point is calculated according

to Equation 5-7 (Simpson’s method).

i

1j

1jj1ji vv4v6dta , (5-7)

where vj is the angular velocity in j-th sampled point and dt is the sample interval (0.02 s for

50 Hz sampling rate).

Because there are three axes in the signals of hand grasps, the hand grasp angles should be

calculated in three axes separately.

5.3.3 Processing Methods of Rigidity Parameters

At first, the IMU and force sensor boxes should be calibrated and verified.

Mechanical impedance is a mathematic model used to describe the dynamic features of the

linear vitiation system. For a stable linear-vibratory system, its outputs (displacement, veloci-

ty, and acceleration), under a simple harmonic and alternating force are the harmonic move-

ments with the same frequency. There is, however, a delay in phase. Mechanical impedance is

a feature of frequency response. The system outputs depend on the features of the system such

as inertia, velocity, stability, and stiffness.

The viscous and elastic components, which are the main components of the mechanical im-

pedance, are modeled in this study (Charles, 2003).

Rigidity assessment is performed by the measurement of elbow angle movement and torque

on the wrist (Patrick et al., 2001). The outputs of two force sensor boxes and an IMU around

the wrist area are:

System Concept

49

αa ,,, 21 FF , (5-8)

where and are the outputs of force sensor box 1 and force sensor box 2, respectively; a

and are the acceleration and angular velocity of elbow movement, respectively. Their units

are N, g, and rad/s, respectively.

The triple-axis elbow angles (α) during the rigidity task can be calculated from the IMU out-

puts ( αa , ) in real-time using the DCM algorithm (Madgwick et al., 2011).

The calculation of elastic stiffness (c) and viscosity (d) is realized by using a least squares

parameter estimation method (regression analysis) to solve Equation 5-9 with ten-second data

(Patrick et al., 2001).

The torque (T) in a rigidity assessment task can be expressed as:

edcl)FF(T 21 αα , (5-9)

where l is the length of the subject’s arm, c is the elastic stiffness of the subject’s elbow, d is

the viscous stiffness (viscosity) of the subject’s elbow, and e is the constant offset of the sen-

sors. The arm length (l) must be determined before the measurement, and set in the system’s

user interface through keyboard entry.

With ten-second duration and 100 Hz sampling rate, a 1000-point data array with the format

of Equation 5-8 is obtained during a single rigidity assessment task. Equation 5-9, which is

the model of the elbow joint movement, can be written as:

α α . (5-10)

Therefore, Equation 5-10 can be written in vector form as:

, (5-11)

where the components of Equation 5-11 are:

α α (5-12)

(5-13)

After the angular displacement calculation of the elbow’s passive movement, the angular ve-

locities and elbow angular displacements are combined in matrix A. Angular velocity ( ) is

the first column in matrix A. On the condition that the elbow moves exactly in one direction,

the three-dimensional angular displacement and angular velocity of elbow movements can be

replaced with single-axis data points in the Equation 5-8. In addition, the combination of all

sensors and axes needs further signal proceeding, such as fuzzy analysis, classification and

regression trees, artificial neural networks, support vector machines, or other pattern recogni-

tion techniques.

Thus, a least squares estimation is obtained using the following equation:

. (5-14)

Mechanical impedance is the feature of the parkinsonian rigidity, and is calculated as follows:

System Concept

50

, (5-15)

where f is the frequency of hand movement and is obtained with a peak-detection algorithm

from the angular displacement of elbow movement; and fd 2 is the modified viscous

stiffness.

In order to acquire the relation between mechanical impedance and the UPDRS ratings, the

relation of elastic stiffness and viscosity with the rigidity severity should first be investigated.

Six-Axis Direction-Cosine-Method Algorithm

Direction cosine method is another way to construct a rotation matrix, other than Euler angles

(Edwan et al., 2011).

The six-axis DCM algorithm is based on a triple-axis gyroscope and a triple-axis accelerome-

ter. The gyroscope is used as the primary source of orientation information. The accelerome-

ter is used for roll-pitch drift correction because it has no drift over time. Only the gravity

vector of the accelerometer is used for the drift detection.

Figure 5-16: Block diagram of DCM algorithm. Here PI controller denotes a proportional-integral con-

troller. For six-axis DCM algorithm, there is no yaw-axis input for the drift detection.

As shown in Figure 5-16, a proportional plus integral (PI) controller is used to control the

drift adjustment. Each of the rotational drift correction vectors (roll and pitch) is multiplied by

weights and fed to a PI feedback controller. Then the drift adjustments are added to the gyro-

scope vectors to produce corrected gyroscope vectors. The outputs of the algorithm are three-

dimensional angles (orientation).

α α

α

a

Compass or GPS

Drift adjustment Integration & Normalization Orientation

Drift detection

Gravity detection

PI controllerError

Gravity

dt.

Gyroscope α

Adjustment

Patch & Roll

Yaw

Accelerometer

α

Prototypical Realization

51

6. Prototypical Realization

At first, the realization of the nine-axis DCM algorithm is presented in Chapter 6.1. In addi-

tion, two tremor and bradykinesia assessment systems with wireless communication inter-

faces, which have already been implemented at the beginning of this study, are presented in

Chapter 6.2. These systems could be used in patients’ homes and in future versions.

After that, prototypical realizations of the tremor/bradykinesia assessment system and rigidity

assessment system are presented. The materials, hardware configuration, and software de-

scription of these two systems are presented in Chapters 6.3, 6.4, and 6.5, respectively. As

described in Chapter 6.6, inertial sensors and force sensor boxes of these systems were cali-

brated according to the sensor calibration procedures.

At last, the realization of the combined system, which can assess the three primary symptoms,

is presented in Chapter 6.7.

6.1 Nine-Axis Direction-Cosine-Method Realization

As Figure 5-16 in Chapter 5.3.3 shows, signals of GPS (global positioning system) or a triple-

axis compass can be used to reduce yaw drift. Such a multi-sensor fusion method, which in-

cludes the signal processing of a triple-axis compass, a triple-axis gyroscope, and a triple-axis

accelerometer, is a nine-axis DCM algorithm.

The nine-axis DCM algorithm can be realized in an eight-bit MCU of small size. A prototype

based on the nine-axis DCM algorithm and an eight-bit MCU has been implemented.

Figure 6-1: Atmel Inertial Two Sensor Board (AVRSBIN2) from Atmel Inc., USA (© Atmel, 2011).

As shown in Figure 6-1, an Atmel Inertial Two Sensor Board (AVRSBIN2), which delivered

a full nine-DOF inertial sensor platform, was used to supply the sensor fusion algorithm. This

circuit board combined an accelerometer (KXTF9, Kionix Inc., USA), a compass

(HMC5883L, Honeywell Inc., USA), and a gyroscope (IMU-3000, InvenSense Inc., USA).

Another circuit board, which included a microcontroller (ATMEGA644, Atmel Inc., USA)

and other electronic components, was used to run DCM algorithm.

Prototypical Realization

52

The sampling rates of both the gyroscope and accelerometer were 50 Hz, while the sampling

rate of the compass was 10 Hz. The gyroscope measured rapid movement, while the accel-

erometer and compass removed the drift in the gyroscope signal.

The three-axis angles, which were the results of sensor fusion using the DCM method, were

sent to a computer in 50 Hz. As shown in Figure 6-2, a 3D demo displayed the rotational

movement of the sensor board (AVRSBIN2). This demo program was modified based on the

program of Julio et al. (2009).

Figure 6-2: Nine-DOF AHRS demo based on Python 2.6.4. Python is a simple but powerful open-source

programming language.

6.2 Prototypes with Wireless Communication Interfaces

Two prototypes with wireless communication for tremor/bradykinesia assessment are pre-

sented in this section. A prototype was for the motion tracking of the wrist and all fingers,

while the other for the motion tracking of a single finger.

At first a glove-based system with several accelerometers and gyroscopes was used to meas-

ure the hand’s motion. As shown in Figure 6-3, the desired hardware included sensors, a re-

chargeable battery, and a microcontroller. These electronic components were embedded in a

small case in the same fashion as a wrist blood pressure monitor. The case incorporated con-

nectors to the sensor module. The sensors were able to be attached and removed from the fin-

gers without difficulty.

The IMU3000 gyroscope by Invensense Inc., with the dimensions of 4mm 4mm 0.9mm,

and the SMB380 accelerometer (3mm 3mm 0.9mm) by BOSCH, and the MMA8452Q

accelerometer (3mm 3mm 1mm) by Freescale were chosen for use in this design.

Prototypical Realization

53

These inertial sensors were placed on the upside of a washable conductive textile (Textronics

Inc.). Five of these conductive textiles were able to be attached on each finger of a hand. The

conductive textile on the thumb was connected to the power supply (level “1”), while the

conductive textiles on other fingers were connected to the circuit ground respectively via a

resistor (level “0”). The communication between the microcontroller (ATMeaga644) and sen-

sors was achieved with a two-wire serial interface (TWI) multiplexer (PCA9546A, TI, USA).

Two NanoLOC AVR Modules (microcontroller and 2.4 GHz radio transceiver, Nanotron

Technologies GmbH, Germany) were used for the communication between the microcontrol-

ler and the computer. All the electronic components in the wrist were embedded into the cuff

of a wrist blood pressure monitor and powered by a lithium-ion battery (3.7 V, 1400 mAh).

The NanoLOC AVR Module included an Atmel AVR microcontroller, which managed the

sensors and sent the data to a computer via wireless communication. Another NanoLOC AVR

Module, which was plugged into a computer, received the data and sent it to the computer via

RS232 to USB transmission.

a) b) c) d)

Figure 6-3: Components of the measurement system with wireless communication. a) NanoLocAVR Mod-

ule (35mm × 14mm × 3mm); b) lithium-ion battery; c) touch sensor based on a washable conductive tex-

tile; d) the cuff from a blood pressure monitor (Beurer BC19, Beurer GmbH, Germany). A NanoLocAVR

module, lithium-ion battery, and other additional components were placed inside the cuff of this blood

pressure monitor.

Figure 6-4 shows the hand motion measurement system and the sensor module which can

measure the movements in all fingers and the wrist.

a) b)

Figure 6-4: Hand motion measurement system with sensor modules (inertial sensors) and touch sensors.

a) hand motion tracking system; b) schematic of a touch sensor. Each touch sensor’s output was connect-

ed to a digital input pin of the microcontroller. The sensor module can be attached to the fingers and con-

nected to the command module

Touch sensor

(ground)

Sensor

modules

Touch sensor

(outputs)Touch sensor (ground)

Touch sensor

(output)

Conductive

textile

Prototypical Realization

54

Figure 6-5 shows a prototype with the wireless communication interface. Rest, postural, and

action tremor tasks, together with the finger taps task, can be assessed by this system. One

disadvantage of this system is that it cannot be used in an operating room.

Figure 6-5: Wireless tremor and bradykinesia assessment monitor with a single sensor module. Its case

was also based on a blood pressure monitor (Beurer BC19, Beurer GmbH).

6.2.3 Graphical User Interfaces

Figure 6-6 describes the GUI of the hand motion measurement system for tremor and

bradykinesia assessments with four sensor modules, which were located on the wrist and three

fingers. This system could be used for bradykinesia and tremor assessments.

Figure 6-6: GUI of the hand motion tracking system (for finger tapping task). Finger 1 in this figure

means the index finger. This GUI corresponds to hardware in Figure 6-4.

The raw data from each axis of the gyroscope and the combined value of all three axes’ out-

puts of the accelerometer, together with the touch sensor signals, were displayed in waveform

mode.

The dominant frequencies and the estimated powers of two fingers’ movements were comput-

ed and displayed directly. The auto power spectrum of each finger movement was displayed

at all times. When the dominant frequency and its estimated power met the definition (fre-

quency range and amplitude threshold), the tremor indicator would light up. The display was

refreshed at 0.05 second intervals.

To save the dominant frequency and power values, the key labeled “Acquire Data” was

pressed.

Prototypical Realization

55

Finger tapping and supination-pronation movements were used to assess bradykinesia based

on the hand motion measurement system. The change in angles and the time taken for the two

fingers to come into contact and separate were displayed in real-time. The raw data of all sen-

sors were stored in the computer in real-time.

Figure 6-7 represents the GUI of playback mode for this measurement system. The stored raw

data were able to be opened and replayed with this user interface.

Figure 6-7: Review of the stored sensor signals in a tremor or bradykinesia assessment task of the hand

motion tracking system (playback mode). This GUI corresponds to the hardware (Figure 6-4) and soft-

ware (Figure 6-6).

Figure 6-8 and Figure 6-9 show the GUIs of the prototype in Figure 6-5, which only had a

sensor board and a wireless communication interface. These GUIs were based on LabVIEW

2010 Evaluation Version (National Instruments Corp, USA) and Visual C# 2010 (Microsoft

Inc., USA) respectively.

Figure 6-8: LabVIEW-based GUI of the wireless tremor and bradykinesia assessment monitor with a

sensor board. This GUI corresponds to the wireless tremor and bradykinesia assessment system in Figure

6-5.

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56

Figure 6-9: Visual C#-based GUI of the wireless tremor and bradykinesia assessment monitor with a sin-

gle inertial sensor module. This GUI also corresponds to the wireless tremor and bradykinesia assessment

system in Figure 6-5 (© TUM-MIMED, 2013).

As can be seen from Figure 6-8 and Figure 6-9, the built-in graphical user interface compo-

nents, such as buttons and graphs, make LabVIEW more intuitive for displaying signals and

results. It is also easy to modify the LabVIEW program. Thus, the realization of a test version

using LabVIEW has some advantages. The program, which is based on Visual C#, is more

readable and is better to be embedded in a commercial system considering its lower price.

6.3 Materials

6.3.1 Selection of Sensors

The first step in implementing the glove monitoring systems was to select the appropriate sen-

sors.

Sensor Specifications

According to the literature (Giuffrida, et al., 2009; Heldman, et al., 2011a; Patrick, et al.,

2001), the full-ranges of sensors in the neurological symptom assessments are listed as fol-

lows:

Angular velocity: ± 2000 º/s (degrees per second or dps) in three dimensions.

Acceleration: ± 4 g in three dimensions; here g is the gravitational acceleration (1g =

9.8 m/s2).

Attached force on both sides of the wrist: 0–80 N; here N is Newton (1 Newton

=101.97 grams).

MEMS IMUs and FSR sensors were chosen in this project for their small dimensions and

low-cost compared to other types of sensors.

Figure 6-10 shows the inertial sensors involved in this study.

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57

Figure 6-10: Inertial sensors used in this study (IMU3000 /MPU6050 /MPU6150 /MPU9150 /MMA8452Q

/SMB380) (© Invensense, 2013; © Freescale, 2012; © Bosch, 2010).

The Standardized Sensor Performance Parameter Definitions for MEMS sensors, which was

organized by Intel, Qualcomm, and the MEMS Industry Group (MIG), was not released until

May 2013. Therefore, not all of the primary parameters of inertial sensors were available in

the datasheets.

The primary features of available inertial sensors and IMUs are listed in Table 6-1. These

parameters were taken from the relative datasheets. These sensors are from manufactures such

as Invensense, Bosch, ST, Kionix, and ADI, respectively.

Table 6-1: Comparative specifications of inertial sensors. Here “⁄” means no embedded sensor fusion;

“–” means no available information; Gyro. refers to a 3-axis gyroscope and Acc. denotes a 3-axis ac-

celerometer.

Sensor

Embedded

sensor

fusion

Package

Current (mA)

[Gyro.; Acc.]

(total)

RMS Noise

[Acc.; Gy-

ro.]

Resolution

(bits)

[Gyro.; Acc.]

Release

date

IMU3000

Digital

Motion

Processor

440.9

mm; QFN 6.1

0.1°/s rms,

0.01º/s/√Hz 16 05/2010

SMB380 ⁄ 330.9

mm; QFN 0.2 500 µg/√Hz 10 09/2007

MPU6050

Digital

Motion

Processor

440.9

mm; QFN 3.6; 0.5; (3.9)

400µg/√Hz;

0.05°/s rms 16;16 11/2010

MPU6150

Digital

Motion

Processor

440.9

mm; QFN 3.6; 0.5; (3.9)

400µg/√Hz;

0.2°/s rms 16;16 11/2010

LSM330 iNEMO 33.5

1mm; LGA 6.1; 0.25 – 16; 16 07/2012

BMI055 ⁄ 34.50.95

mm; LGA (5.15)

150µg/√Hz;

0.014°/s/√Hz 16; 12 09/2012

KXG02 ⁄ 440.9m

m; LGA

3.75; (4.0)

150 µg/√Hz 16; 16 01/2012

ADIS163

67 ⁄

232323

mm; ML-

24-2

49 0.5mg//√Hz;

0.044°/s/√Hz 12; 12 01/2010

An IMU works by detecting both the current rate of acceleration (using a 3-axis accelerome-

ter) and the changes in rotational attributes (using a triple-axis gyroscope). At the beginning

Prototypical Realization

58

of this study, a triple-axis MEMS gyroscope and a triple-axis MEMS accelerometer were used

to constitute an IMU. After the availability of single-chip IMUs, MPU6150 and MPU6050

were chosen in this project for the following reasons:

Their pin-out and package (QFN) are compatible with the IMU3000, which we had

used before the availability of IMU chips. MPU 6000 and MPU6150 were first availa-

ble on the market as single-chip IMUs.

A chip with QFN package is better to solder than the chip with LGA package in labor-

atory conditions.

Their outputs are digital, obtained via a TWI/ SPI (serial peripheral interface) interface

by the MCU.

MPU6150 is actually a lower cost version of the MPU-6050 and is focused on television re-

mote control and gaming applications. MPU-6050 (or MPU-9150) has better performance

with regard to bias offset, drift rate, and noise levels.

Figure 6-11 shows the system structures of MPU6150 and MPU9150, which has an additional

compass layer.

Figure 6-11: Structure of the multi-sensor integration (© Invensense, 2013). Six-axis MPU6150 has a die of

two layers, while the nine-axis MPU9150 has a three-layer die.

Figure 6-12 shows the system diagram of MPU6150, which is same as MPU6050.

Figure 6-12: Block diagram of MPU6150/MPU6150.

Calibration

Sensor RegistersSensor RegistersSensor RegistersSensor

Registers

FIFO

Digital Motion Processor

(DMP)

2-wire SerialInterface

MPU6X50

TWI

LDO3.3VX Accel.

Y Accel.

Z Accel.

X Gyro.

Y Gyro.

Z Gyro.

Temp. sensor

ADC

ADC

ADC

ADC

ADC

ADC

ADC

ADC

Sig

na

l Co

nd

ition

ing

Prototypical Realization

59

A gyroscope has much higher power consumption than an accelerometer or a compass in

working state, more specifically, 3.6 mA for a triple-axis gyroscope compared to 0.2 mA for a

three-axis accelerometer inside the Invensense MPU6050 or MPU6050.

Figure 6-13 shows the axis orientations of MPU6050 and MPU6150.

Figure 6-13: Axis orientations of MPU6150 and MPU6050. The benefit of this structure is that the axes of

the accelerometer and gyroscope are the same. Thus the adjustment of axes before sensor fusion is not

needed.

The main part of a force sensing resistor (FSR) is a conductive polymer, which detects physi-

cal pressure or force applied in the upside. FSRs are simple to use due to a low cost and small

thickness. The disadvantages are their low precision (from 5% to 25%) and possible damage

by continuously being attached with pressure (for several hours). The major manufacturers of

FSRs are Interlink Electronics Inc., Tekscan (Flexifore sensors), and IEE International Elec-

tronics & Engineering Inc., etc. The information regarding the FSRs, which are suitable for

this project, is listed as Table 6-2.

Table 6-2: Comparative information of FSRs. Here “–” means no available information. For the

FSR402, the best force range is from 0 to 20 N, but it can be extended to 100 N when applying force

evenly over the active area (0.125 cm2) of its surface area.

Sensor Range

(N)

Repeat-

ability

Linearity

(Error)

Hystere-

sis

Response

time Active area

Current

density

FSR402 0–20N* +/- 2% – +10% 3 ms Ø12.7 mm < 1 mA

FSR149N

S 0–100N ± 3% – 20% 2–3 ms Ø6 mm < 1 mA/cm

2

FSR152 0–100N ± 3% – 20% 2–10 ms Ø15.2 mm < 1 mA/cm2

A401 0–110N ±2.5% ±3% 4.5 % 5µs

(5 ms) Ø25.4 mm < 2.5 mA

These sensors are robust polymer thick film (PTF) sensors that exhibit a decrease in resistance

to an increase in force applied to the surface of the sensors. They are with analog outputs. The

output of an FSR should be connected to the ADC pin of a microcontroller.

One disadvantage of the FSRs is that they can provide only single-axis analog output, but the

elbow movement during the rigidity assessment task is a three-dimensional motion. MEMS

three-axis force sensors have appeared with digital interface but as of yet there is not a fully-

developed product available for our project. For example, a capacitive three-axis force sensor

(WEF-3A) from Wacoh Inc., Japan, is very small (Ø: 10mm, H: 7mm) and has a built in 32-

bit ADC, but the operating current is 200 mA. In the future, MEMS three-axis force sensors

can be used in this project.

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60

Figure 6-14 shows the structure of a round FSR. Figure 6-15 shows the photo of FSR149NS

and FSR 402 short.

Figure 6-14: Structure of a round FSR (© Interlink, 2012).

Figure 6-15: FSR149NS and FSR 402 short.

6.3.2 Additional Components

As Figure 6-16 shows, a series of TG®

medical gloves (L&R GmbH, Germany) can be worn

by different patients. These gloves have the advantage of being washable, hard-wearing com-

fortable, and have high durability. They are also CE marked and sterilizable. In addition, they

meet the sterilization standards in DIN EN ISO 11135, 11137, and 17665.

Figure 6-16: Medical gloves made by Lohmann & Rauscher TG, with four different sizes.

The microcontroller selected for the tremor/bradykinesia assessment system was an

ATMega1284P, while the microcontroller selected for the rigidity assessment system was an

ATMega328. Both these chips were manufactured by Atmel Inc., USA.

The missing sampling data have a very bad effect on the PSD results (Spieker et al., 1995).

The bit rate (or call baud rate) for the serial communication was set to 115200 bps (bits per

second) or higher. The crystal oscillator frequency is better at 7.3728 MHz, 11.0592 MHz,

18.432 MHz or 22.118 MHz. Thus, the bit error rate of serial communication between the

MCU and command module is about zero.

Prototypical Realization

61

The speed grades of both the ATMega1284P and ATMega328 are:

0 10 MHz @ 2.7 5.5V.

0 20 MHz @ 4.5 5.5V.

The crystal oscillator frequencies of the two prototypes for the tremor/bradykinesia assess-

ment and the rigidity assessment were higher than 10 MHz. Therefore, their power supplies

were chosen to be +5 VDC.

FT232R (Future Technology Devices Inc., UK) is a USB to serial UART (universal asyn-

chronous receiver/transmitter) interface which was to be used here.

Figure 6-17: Alarm control cable LiYY 4 × 0.14 mm².

As Figure 6-17 shows, a shielded four-pin cable was used to connect the sensor board to the

command module.

6.4 Physical Implementation

Figure 6-18: Internal architecture (hardware) of the glove monitoring system.

MPU6150(Gyro_ /Accel_)

MCU(ATMEGA1284)

TWI

Voltage converter (LDO 3.3V)

AVR_USB JTAGConnector

Sensor board

Command Module

JTAG

USB

5V

data

Power

Forcesensors

Rigidity cuff

MCU(ATMEGA328)

Voltage converter (LDO 3.3V)

ISP Connector

MPU6150(Gyro_ /Accel_)

MPU6150(Gyro_ /Accel_)

MPU6150(Gyro_ /Accel_)

Prototypical Realization

62

Figure 6-18 shows the hardware diagram of the glove monitoring system, which included two

parts. The command module was for the tremor and bradykinesia assessment, while the rigidi-

ty cuff was for the rigidity assessment.

6.4.1 Prototype for Testing

At first, the command module was supposed to be integrated into the textile glove. As shown

in Figure 6-19, the command module was fixed in the glove with a durable elastic fabric Vel-

cro strap. A USB 2.0 type A to Mini-B five-pin cable was used to connect the command

module to a computer.

Figure 6-19: Photo of the textile-integrated command module and mini USB connector.

One disadvantage of this design was that the circuit part of the command module could come

into contact with the human body. Another disadvantage of the design was that the USB con-

nection was not stable.

Thus the command module was placed in a case for reasons of safety. According to the exper-

iments by Lorenzo et al., a USB JTAG cable (Lorenzo et al., 2011) was chosen for its better

connection stability.

6.4.2 Tremor/Bradykinesia Assessment System

Figure 6-20: Internal architecture of the tremor/bradykinesia assessment system.

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63

As Figure 6-20 shows, the command module and the sensor board were used for finger mo-

tion data acquisition and communication with a computer. The command module acquired

sensor data and sent it to the computer. An IMU (MPU6150) was located on the sensor board.

The implementation of the sensor board is shown in Figure 6-21 and Figure 6-22. The sensor

board (IMU) required a +3.3 V power supply and a TWI bus. Then a four-pin cable connected

the sensor board with the command module.

Figure 6-21: Schematic of the sensor board.

Figure 6-22: Photo of the sensor board (dimensions: 14mm × 11mm × 2mm).

As Figure 6-22 shows, the sensor board was put into a liquid insulation can (PLASTI DIP

LIQUID TAPE, Plasti Dip® GmbH, Germany) for insulation protection. The board was in-

side the can for one minute and then taken out to cool for three hours.

Figure 6-23 shows the circuit board and the housing of the command module. Three screws

were placed in the top side of the housing. The implementation of the command module is

shown in Figure 6-24 (a).

a) b)

Figure 6-23: Photos of the circuit board and housing of the command module. a) command board; b)

housing with four screws (M2 × 6mm). There was also a prototype with only three screws.

Prototypical Realization

64

Figure 6-24 shows the command module with the sensor board and a USB JTAG cable. The

USB JTAG cable, which includes a serial-to-USB chip (FT232R, Future Inc., UK), was con-

nected to a female 30-pin connector.

a) b)

Figure 6-24: Photos of the command module and USB JTAG cable. a) photo of the command board with

the sensor board; b) USB JTAG cable for the communication between the command module and a com-

puter.

There were five connectors on the circuit board of the command module. A female 30-pin

connector was used to connect the command module to a computer via a JTAG USB cable.

The JTAG interface of the microcontroller was connected to this connector for programming.

A four-pin connector was used for the connection with the sensor board. Two connectors

were available for the force sensor boxes but were not used in this version. The USB port of

the computer provided the power supply (+5 V) for the command module. The voltage was

regulated from 5.0 V to 3.3 V with a low dropout linear regulator (XC6204B332MRN, Torex

Inc., Japan).

The IMU in the sensor board was connected to the microprocessor (ATMega 1284P, Atmel

Inc., USA) via the TWI interface. The microprocessor sampled sensor data at 100 Hz or 50

Hz. A green light-emitting diode (LED) also indicated the state of sensor readings at 100 Hz

or 50 Hz.

The final implementation of the tremor/bradykinesia assessment system is shown in Figure 6-

25.

a)

b)

Figure 6-25: Overview of the prototype for tremor/bradykinesia assessment. The command module was

34mm × 32mm × 7mm. The command module and sensor board both had indirect contact with the human

body.

USB JTAG cable

Command module

Textile glove

Sensor board

Prototypical Realization

65

The operation of the wired system for tremor/bradykinesia is shown in Figure 6-26.

Figure 6-26: Tremor/bradykinesia assessment system.

6.4.3 Rigidity Assessment System

The rigidity assessment system was implemented before the availability of single-chip IMUs

such as MPU6150/6050. Therefore, two inertial sensors were adopted. A triple-accelerometer

(MMA8452Q, Freescale Inc., USA) worked with a triple-gyroscope (IMU3000, Invensense

Inc., USA).

Two differential force sensor boxes were used to measure the attached force on the wrist.

Each force sensor box was built from four FSRs (FSR-149NS, IEE Inc., Luxembourg), which

were connected in parallel. Each force sensor box had only one output, with a type of voltage

divider.

Figure 6-27 shows the realization of a force sensor box. The output of the force sensor box

(Vout) was directly connected to the ADC input of a microcontroller (ATMega 328p, Atmel

Inc., USA).

a) b)

Figure 6-27: Implementation of a force sensor box. a) schematic of the force sensors; b) photo of the force

sensor box (Based on Dai et al., 2013a).

A plastic case was used to place the force sensor boxes and can be pressed by an examiner

during rigidity assessment. The rigidity cuff consisted of a plastic housing, a Velcro textile

band, and a microprocessor board.

Figure 6-28 shows the Velcro textile band. Through a combination of hard Velcro and soft

Velcro, the two force sensor boxes and the microcontroller board were fixed on the wrist. The

microcontroller board and the housing of the two force sensor boxes were all attached on the

Prototypical Realization

66

outside of the band. The textile band had the advantage of being able to fit the different arm

diameters of each patient.

Figure 6-28: Photo of the Velcro textile band (© TUM-MIMED, 2012).

Because the power supply of the microcontroller (5 V) differed from that of the inertial sen-

sors, a dual bi-directional TWI bus voltage-level translator (PCA9306, TI Inc., USA) was

used. Figure 6-29 shows the schematic diagram of the voltage-level translation circuit.

Figure 6-29: Schematic of the dual bi-directional TWI bus voltage-level translator.

Figure 6-30 shows the circuit board of the rigidity cuff. The implementation of the command

module is shown in Figure 6-31. All the sensor data were sampled at 100 Hz by the micropro-

cessor.

Figure 6-30: Photo of the microcontroller board in the rigidity cuff.

Figure 6-31: Overview of the prototype for rigidity assessment (Based on Dai et al., 2013b).

The prototypes of both the tremor/bradykinesia and rigidity assessment system are shown in

Figure 6-32.

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67

Figure 6-32: Prototypes of the glove monitoring system. 1) command module (tremor/bradykinesia as-

sessment part); 2) rigidity cuff (rigidity assessment part). The command module and sensors did not have

direct contact with the human body (Taken from Dai et al., 2013a).

6.5 Graphical User Interface Implementation

The data transmission, signal processing, and GUI of the tremor/bradykinesia assessment sys-

tem were carried out on a computer using LabVIEW 2010 Evaluation Version (National In-

struments Corp., USA). Figure 6-33 shows the GUI of the bradykinesia and tremor assess-

ment system.

Figure 6-33: GUI of the tremor/bradykinesia assessment system. 1) serial interface setting; 2) raw data

path configuration; 3) start button; 4) progress bar; 5) results recording list; 6) real time result area; 7)

sensor raw data; 8) results recording chart; 9) PSD chart.

The GUI included the task start buttons, progress bars, recording lists, and the storage loca-

tion setting of raw data.

To start the tremor assessment, the task button in the GUI must be set to “Tremor” and the

examiner needs to press the start button. During tremor assessment, the raw data and the PSD

figure of three-second signals were displayed at the bottom. At the end of the tremor task, the

actual assessed results, which included dominant frequency and tremor amplitude during the

ten-second assessment task, were displayed in the result area of tremor assessments.

To start bradykinesia assessment, the task button in the GUI must be set to “Kinesia”. During

bradykinesia assessment, the raw data and the PSD figure as well as the angular displacement

were displayed at the bottom. At the end of the bradykinesia task, the actual assessed results,

1

2

1

2

3

4

5

6

7

8

9

Prototypical Realization

68

which included dominant frequency, mean and standard deviation of the grasping ranges,

were displayed in the result area of bradykinesia.

After each tremor or bradykinesia assessment task, the corresponding assessment result item,

which consists of the assessment number and relative parameters, was added to the results

recoding list automatically. The result items with the biggest and smallest amplitudes were

displayed at the bottom of the results recoding list. All the raw sensor data were stored in the

computer for offline analysis.

Data communication and the GUI of the rigidity assessment system were realized with Visual

C# 2010 (Microsoft Inc., USA). This program invokes MATLAB 2012b (MathWorks Inc.,

USA) to perform signal processing. Figure 6-34 shows the GUI of the rigidity assessment

system.

Because the patient’s forearm length could not be measured directly with the above sensors,

the examiner should record the subject’s forearm length in the input box of the GUI.

Figure 6-34: GUI of the rigidity assessment system (Baed on Dai et al., 2013b). 1) raw data path configura-

tion; 2) serial interface setting; 3) start button; 4) input box for the patient’s forearm length; 5) progress

bar; 6) communication status; 7) results.

6.6 Calibration of Inertial Sensors and Force Sensor Boxes

The calibration methods for inertial sensors are first introduced in Chapter 6.6.1. The sensors

in the rigidity cuff were first calibrated. However, the calibration of the tremor/bradykinesia

assessment system depends on each IMU on the sensor boards. There are several sensor

boards available. Each IMU should be calibrated respectively.

6.6.1 Analysis of Inertial Sensors

The key issue in the performance of a motion tracking system is its accuracy. Although pre-

sent inertial sensors are smart sensors (factory calibration and run-time calibration firmware

are embedded into the sensors); bias, noise, scaling factor error, and the cross-axis misalign-

ment compensation matrix still require consideration. Some initial calibration procedures for

individual gyroscopes and accelerometers are needed.

Prototypical Realization

69

Gyroscope Calibration

The single axis output (angular velocity ) of a gyroscope in time t is expressed as fol-

lows:

, (6-1)

where is the true angular velocity, a and b are the scaling and biasing of the output, re-

spectively. The output bias error (offset) is related to its special structure, temperature change,

magnetic field, and other noises. Linear acceleration can also result in bias in a gyroscope. In

general, this bias error results in big drifts after an integration time.

Thus the first-order triple-axis gyroscope compensation equation is shown as follows:

, (6-2)

where Sgx, Sgy, and Sgz are the scaling factors in three axes; , , and are the offsets

in three axes; and M is the 3×3 cross-axis misalignment compensation matrix.

In this study, the offsets are calculated with the mean gyroscope outputs in a static state in ten

seconds. The calculation function is described as follows:

, (6-3)

where , , and are the raw data outputs in static state; N is the number of sampled

points (= sampling rate × 10 seconds).

The true angular velocities ( , , and ) from a gyroscope are considered to be linear

according to the datasheets of gyroscopes. The sensitivity of a single gyroscope axis is com-

puted with the values recorded while rotating the gyroscope 180° around that axis. The angu-

lar displacement by integrating the angular velocity is a sinusoidal signal. Those values are

integrated over time and multiplied by 1/(sample rate). The signals of each gyroscope axis are

then divided by the scaling factor to obtain the angular velocity in [°/s]. Then the scaling fac-

tors in three axes are described as:

, (6-4)

where is the gyroscope’s sensitivity scaling factor [unit: count/(°/s)], which can be

found in the datasheet; and is the sampling interval (unit: s).

Accelerometer Calibration

Accelerometer offsets, scaling factors, and cross-axis misalignment errors should also be

compensated by the calibration process.

Thus the first order triple-axis gyroscope compensation equation is shown as follows:

Prototypical Realization

70

, (6-5)

where Sax, Say, and Saz are the scaling factors in three axes; , , and are the offsets

in three axes; and M is the 3×3 cross-axis misalignment compensation matrix.

Figure 6-35: Positions of an IMU for calibrating accelerometer offsets.

The outputs of a triple-axis accelerometer include linear accelerations and gravitational accel-

erations. For the calibration of static offsets, the sensor is in a stable condition before being

measured. Each axis of the accelerometer is placed parallel to gravity with both positive and

negative sides. As Figure 6-35 shows, the true value of the related acceleration in the static

state should be 1g or -1g. However, the outputs of the accelerometer include also bias (off-

sets). The recorded gravitational acceleration both with and against gravity, as shown in Fig-

ure 6-35, are described as follows:

(6-6)

The triple-axis accelerometer outputs are scaled to multiples of the gravitational constant (g).

These values are used to calculate the sensitivity and offset of each axis. Then the offsets can

be calculated with the values in Equation 6-2 as follows:

=

(6-7)

The scaling factors can be calculated with the values in Equation 6-2 as follows:

=

(6-8)

where is the sensitivity (unit: count/g) of the accelerometer.

+g -g +g -g

+g -g

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71

The sensor outputs should be sampled at room temperature and the sensors should have been

working for at least one hour before the measurement.

6.6.2 Calibration of Inertial Sensors

According to the calibration procedures in Chapter 3.6.3, the calibration results of the gyro-

scope (IMU3000) and the accelerometer (MMA8452Q) in the rigidity cuff are shown from

Table 6-3 to Table 6-5.

Table 6-3 Offsets and sensitivities of the gyroscope in the rigidity cuff (IMU3000, 16-bit resolution: -

3276832767)

X-Axis Y-Axis Z-Axis

Offset -390 +161 +68

Sensitivity 0.9358 0.9491 0.9611

Table 6-4 Accelerometer outputs when its axes were oriented along/against the direction of gravity

(MMA8452Q, 12- bit resolution: -20482047)

X-Axis Y-Axis Z-Axis

Orientation along g-axis (1g) 991 1011 1008

Orientation against g-axis (-1g) -1005 -974 -997

Table 6-5 Offset and sensitivity for the accelerometer (MMA8452Q, 12-bit resolution: -20482047)

X-Axis Y-Axis Z-Axis

Offset -7 +18 +5

Sensitivity 0.9746 0.9697 0.9785

6.6.3 Calibration of Force Sensor Boxes

According to Figure 5-8 in Chapter 5.2.2, the output of the force-to-voltage conversion was

tied to a measuring resistor in a voltage divider configuration. The output voltage of a force

sensor box (Vout) was shown as:

MFSRccout RRVV /1/ , (6-9)

where Vcc was the power supply (+5 V); RFSR was the resistor value of the force sensor box;

and RM was the value of the pull-down resistor (4.7 KΩ).

Equation 6-9 shows that the output of a force sensor box response for RFSR is similar to an e-

function. The force-resistance characteristic of a single FSR sensor (FSR149NS) is nonlinear

and the response approximately follows an inverse power-law characteristic. However, a line-

ar force-to-voltage relationship is needed in Equation 5-9 in Chapter 5.3.3. Hence, a regres-

sion analysis was applied to get the voltage-force function. Regression analysis was per-

formed with the Curve Fitting Toolbox in MATLAB R2012a (Mathworks Inc., USA). Results

of a two-term Gaussian regression were compared to one-term and four-term Gaussian func-

tions.

Prototypical Realization

72

Since the FSR output is nonlinear and sensor sensitivity varies, every force sensor box has to

be respectively calibrated. As each force sensor box included four FSR sensors but had only

one output, the calibration of FSRs was performed after the implementation of the two force

sensor boxes.

As shown in Figure 6-36, 58 weight loads ranging from 5 grams to 6000 grams were applied

at the center of the top side of each force sensor box. Since FSRs are prone to drift, only the

value up to three seconds after applying the load could be used and the weight was released

from the force sensor box after every single measurement. Therefore, 116 voltage values were

acquired. The correlation of sensor outputs over weights was recorded.

Figure 6-36: Setup of the force sensor box’s calibration. Each force sensor box was tested with weights,

while the weights were placed in the center top side of the force sensor box (Based on Dai et al, 2013a).

If the applied force was lower than 0.5 N, there was no change in the output of the force sen-

sor box. However, for applying forces bigger than 0.5 N, the resistance of the force sensor

box rapidly dropped and reached saturation when the applied force was more than 100 N. The

FSR sensor tutorial (IEE International Inc., Luxemburg, 2011) states that the FSR sensor re-

sponse to higher loads is similar to an e-function. Therefore, a Gaussian function was chosen

for regression analysis:

, (6-10)

where x was the output of the force sensor box and f(x) was the attached force.

To evaluate the performance of the Gaussian function, results were compared with fits for an

e-function and polynomial functions (Abramowitz et al., 1965).

Figure 6-37: Two-term Gaussian regression plot of force sensor box 1: weight (g) versus the output of the

force sensor box 1 (© TUM-MIMED, 2012).

1) Weight

2) Force sensor box

1

2

Force sensor box output (V)

Weig

ht (g

ram

)

0 0.5 1.0 1.5 2.0 2.5

fit

weight vs force sensor box ouput

Prototypical Realization

73

Figure 6-38: Two-term Gaussian regression plot of force sensor box 2: weight (g) versus output of the

force sensor box 2 (© TUM-MIMED, 2012).

Using the MATLAB Curve Fitting Toolbox, one-, two-, and four-term Gaussian, e-Function,

and Polynomial regression analyses were performed with the measurement data.

Figure 6-37 and Figure 6-38 show the regression plots of a two-term Gaussian regression

function which fitted to the two force sensor box outputs, while Figure 6-39 shows a one-term

Gaussian regression fitting and Figure 6-40 shows a four-term Gaussian regression fitting of

the force sensor box 1.

Figure 6-39: One-term Gaussian regression plot of force sensor box 1: weight (g) versus output of the

force sensor box 1 (© TUM-MIMED, 2012).

Figure 6-40: Four-term Gaussian regression plot of force sensor box 1: weight (g) versus output of the

force sensor box 1. The red circle highlights the area with over-fitting (© TUM-MIMED, 2012).

Force sensor box output (V)

We

ight

(gra

m)

0 0.5 1.0 1.5 2.0 2.5 3.0

fit

weight vs force sensor box ouput

Force sensor box output (V)

We

igh

t (g

ram

)

0 0.5 1.0 1.5 2.0 2.5

fit

weight vs force sensor box ouput

Force sensor box output (V)

Weig

ht (g

ram

)

0 0.5 1.0 1.5 2.0 2.5 3.0

fit

weight vs force sensor box ouput

Prototypical Realization

74

As Figure 6-39 shows, one-term regression did not fit the response curve well, producing low

values of correlation (r2) (see Table 6-6). At the same time, the four-term regression was sus-

ceptible to outliers, which resulted in over-fitting (Figure 6-40).

Table 6-6 shows the results of the different curve fits applied to the data set. The selected fit

has been highlighted. The MATLAB Curve Fitting toolbox was not able to find a one-term

Gaussian fit for the dataset of force sensor box 2. Accordingly, the corresponding value in

Table 6-6 is “no fit”. In addition, there were no four-term e-Function fittings for the two force

sensor boxes.

Table 6-6: Results of regression analysis: adjusted r2 values (force sensor box 1/ force sensor box 2)

Gaussian e-Function Polynomial

1-term 0.9904 / no fit 0.9896 / 0.9925 0.8908 / 0.7778

2-term 0.9937 / 0.9971 0.9931 / 0.9923 0.9826 / 0.9531

4-term 0.9934 / 0.9973 Not available in toolbox 0.9939 / 0.9961

As shown in Table 6-6, a two-term Gaussian function has the highest correlation with the

measured data:

,

(6-11)

where F(vout) was the calculated force value, vout was the sensor box output voltage, and a, b,

and c were coefficients.

The coefficients of Equation 6-11 could also be determined using the MATLAB Curve Fitting

Toolbox. These parameters, which have been calculated using the two-term Gaussian regres-

sion, are given in Table 6-7.

Table 6-7: Coefficients of the two-term Gaussian regression for the two force sensor boxes (FSR box-

es)

a1 b1 c1 a2 b2 b3

FSR box 1 5.91e+04 1104 387.1 1020 322.6 195.5

FSR box 2 1.139e+18 7287 1159 734.6 445.6 253.4

6.7 Combined Version

Together with the tremor/bradykinesia assessment system and the rigidity assessment system,

a portable monitoring system used to quantify all primary neurological symptoms in PD and

ET was able to be implemented.

As shown in Figure 6-41, the combined system was built up based on the two prototypes

(tremor/bradykinesia assessment system and rigidity assessment system).

Some components were replaced for the reason of a higher performance. The crystal oscillator

frequency was set to 18.432 MHz instead of 11.0592 MHz. MPU6050 replaced MPU6150 for

its better gyroscope noise performance. The active area of FSR149NS is too small (Ø4.03

Prototypical Realization

75

mm) and therefore each force sensor box needed four force sensors. FSR402 has a 14.7 mm

diameter active area coverage, thus only one force sensor was needed in the combined ver-

sion.

Figure 6-41: Internal architecture of the combined version.

Figure 6-42 shows the connection between the command module and the force sensor boxes.

The force sensor boxes were connected to the microcontroller via an instrumentation amplifi-

er.

Figure 6-42: Interfaces between the force sensor boxes and command module (MCU board). The outputs

of the two force sensor boxes were connected to an amplifier. TLV2372 (TI Inc., Dallas, USA) is a rail-to-

rail input/output low power instrumentation amplifier.

For the tremor/bradykinesia assessment system and rigidity assessment system, the received

sensor data in the computer were checked with an imitated tremor test by healthy subjects.

The test result indicates that the missing points of sampling data (dropped packets) over the

whole sampled data were less than 0.2% with a sampling rate of 50 Hz. However, sometimes

there were disconnections between the command module and the computer. The assessment

task was able to be interrupted by violent hand movement. In the combined version, one side

of the USB cable was soldered directly to the printed circuit board of the command module,

instead of using a USB connector in the command module.

Prototypical Realization

76

A 10-pin header (FTSH-105-01-L-DV-K-A-P, Samtec Inc., Indiana, USA) was placed on the

center of the command board as the JTAG programming connector, because of its small di-

mension and safe style (keyed).

Figure 6-43 shows the hardware of the combined version.

Figure 6-43: Prototype realization of the combined version.

Figure 6-44 shows the GUI which can be used to assess tremor, bradykinesia, and rigidity.

Figure 6-44: GUI of the combined version (Taken from Dai et al., 2013a).

6.8 Conclusion

The prototype of the tremor/bradykinesia assessment system is presented. It was based on an

IMU, which was attached on a finger, and could be combined with a textile glove.

The first prototype of a portable rigidity assessment system, which was attached to the wrist,

was also presented. It was based on two force sensor boxes and an IMU. As a result, this sys-

tem made it easy to perform passive elbow movement.

The combined version was implemented for quantification of all primary neurological symp-

toms in patients with PD or ET.

For possible hardware design in the future, a microcontroller with an embedded USB com-

munication interface can provide a higher transmission rate between the computer and micro-

Prototypical Realization

77

controller than the serial-to-USB converter. In addition, a system with a wireless interface

instead of wired communication makes the system usable outside of the operating room and

in places such as homes or sickrooms. As a result, the system can be used for bradykinesia

assessment at home or in hospital for further tests.

The signal processing was mainly performed by the computer. The role of the microcontroller

was simply to acquire and send the raw data to the computer.

The Kalman filter and DCM algorithm can be run on a microcontroller. As mentioned in the

first section of this chapter, the nine-axis DCM algorithms can be run on an eight-bit micro-

controller. However, the microcontroller needs more time consummation in this situation.

Thus the sampling rate of the sensors is limited to a low value. In order to perform certain

timing issues such as signal pre-proceeding by the side of the command module, two micro-

controllers may be used: one as the master for sensor data acquisition and the other as the

slave for signal processing. In addition, the sensor fusion can be embedded inside the sensor,

which performs an orientation calculation. Then the angular displacement of tremor, grasping

angles during a bradykinesia task, and elbow angles during a rigidity task can be easily ac-

quired. The angles from the chip-based sensor fusion should be compared to the values ac-

quired with the methods in this system.

Passive elbow movement is a movement in three dimensions. However, the force sensor box-

es provided the different output only in a single axis. Thus the passive movement should be

parallel to one axis of the Cartesian coordinate system.

Tremor/bradykinesia assessment systems with wireless communication interfaces can be used

as mobile medical devices used before and after the DBS surgery. These prototype realiza-

tions are helpful for designs in the future.

Experiments and Discussions

78

7. Experiments and Discussion

IMU do not give position information, but most research projects use raw IMU data for signal

processing in tremors and bradykinesia quantitative assessments. No previous studies have

compared the assessment systems for neurological symptoms based on IMU with other medi-

cal motion tracking systems.

Before comparing this system’s output with the patients’ clinical ratings, it is necessary to

evaluate the calculated technical parameters of this system. In order to do this, several exper-

iments were carried out. Analytical verifications of the three test tasks of the glove monitoring

system were performed. The verifications of analytical methods are presented in Chapters 7.1,

7.2, and 7.3, respectively.

After verifying the system, the tremor and bradykinesia assessment system was tested with

tremor and bradykinesia tasks in the hospital. The experiments of tremor and bradykinesia

assessments are presented in Chapter 7.5 and Chapter 7.6, respectively.

7.1 Verification of Analytical Methods for Tremor Assessment

The NDI Aurora electromagnetic tracking system is first introduced in Chapter 7.1.1. It was

used for the verification of analytical methods for tremor, bradykinesia, and rigidity assess-

ments.

The verification of tremor amplitude and frequency is presented in Chapter 7.1.2.

7.1.1 NDI Aurora Electromagnetic Tracking System

As shown in Figure 7-1 (a), an Aurora electromagnetic (EM) spatial measurement system

(Northern Digital Inc., Waterloo, Canada) was used as the reference system. The Aurora® EM

system is a navigation system designed specifically for medical applications.

As shown in Figure 7-1 (b), the Aurora’s micro six-DOF EM sensor has a small dimension

(2.5 mm L11 mm, line: 2 m) and can be attached to a finger or wrist.

a) b)

Figure 7-1: a) Aurora electromagnetic (EM) tracking system; b) Aurora six-DOF sensor cable tool (©

NDI, 2013).

The Aurora system’s characterized measurement volume can be set to a dome volume (480

mm L660 mm) or cube volume (500mm 500mm 500mm). However, there is a 50 mm

distance between the planar field generator and the characterized measurement volume. Fig-

6 DOF sensor

Planar field generator

System control unit

6 DOF sensor

Sensor interface unit

Experiments and Discussions

79

ure 7-2 shows the characterized measurement volumes. The EM tracking in cube volume

mode has higher accuracy than the dome volume mode. Thus the measurement volume was

fixed to cube volume mode in this study. The accuracy parameters of the EM tracking system

with a six DOF sensor are:

Location: 0.48 mm (RMS), 1.40 mm (95% confidence interval).

Orientation: 0.30º (RMS), 0.48º (95% confidence interval).

In this study, the sampling rate of the EM tracking system was 40 Hz. The IMUs of the glove

monitoring system were sampled at 100 Hz.

Figure 7-2: Characterized measurement volume (left: cube volume mode; right: dome volume mode).

Figure 7-3 shows the experimental setup of all verifications of analytical methods.

Figure 7-3: Setup for the verification of analytical methods. An electromagnetic tracking system was run

with the glove monitoring system at the same time. The graphical user interfaces of both systems were run

on two laptops. The data transmissions of the two systems were both based on serial-USB communication.

7.1.2 Verification of Tremor Amplitude and Frequency

Motivation

IMU signals are angular velocity and linear acceleration; but a surgeon judges tremor severity

according to the displacement of hand tremor. On the other hand, the EM tracking system can

obtain position information directly. Therefore, the comparison of the analytical methods be-

tween EM signals and IMU signals was performed. Here the glove monitoring system refers

to the tremor/bradykinesia assessment system.

Laptop for

EM tracking system

Laptop for

glove monitoring system

Glove monitoring system

and EM sensors

Glove monitoring system

and EM sensors

EM generator

EM control unit

EM sensor unit

1)

2)

3)

4)

5)

6)

1

2

3

5 4

6

Experiments and Discussions

80

Hypothesis

By comparing with those from the EM system, the tremor amplitude (peak powers) and dom-

inant frequency of the glove monitoring system should meet the following requirements:

Mean value and standard deviation of the differences between dominant frequencies:

fmd < 1.00±0.88 Hz (Niazmand et al., 2011b).

Correlation coefficient of peak powers (tremor amplitude): r >0.95 (Bland & Altman,

2003).

Materials

NDI Aurora® EM tracking system with a six-DOF sensor (Northern Digital Inc., Can-

ada)

Tremor/bradykinesia assessment system (included a JTAG USB cable and a command

module with a sensor board)

Laptop installed with the application software of the EM system (Aurora Toolbox)

Laptop with the application software of the tremor/bradykinesia assessment system

(LabVIEW-based GUI for tremor/bradykinesia assessment; MATLAB R2008b for

data analysis)

Method

The dominant tremor frequencies calculated by the tremor/bradykinesia assessment system

and EM system were ftb and fem (see Chapter 5.3.1). Then the difference of their dominant

frequencies in a single tremor task was represented by:

(7-1)

Therefore, fmd (mean and standard deviation of dominant frequency differences) can be calcu-

lated by the values of fd (frequency differences) during all imitated tremor assessment tasks:

± (

, (7-2)

where N was the amount of imitated tremor assessment tasks.

The tremor amplitude (peak powers based on the IMU signals) calculated by the

tremor/bradykinesia assessment system was R, while the tremor amplitude (peak power based

on the position data) judged by the EM system was E (see Chapter 5.3.1). Then the correla-

tion coefficient between these two parameters was:

, (7-3)

where N is the amount of imitated tremor assessment tasks.

Experimental Setup

As shown in Figure 7-4, together with the IMU in the tremor/bradykinesia assessment system,

a six-DOF EM sensor was attached to the subject’s middle finger for real-time finger motion

tracking. Because of the limited EM tracking dimension, only the postural tremor task was

Experiments and Discussions

81

performed by nine healthy subjects (average age: 29±2.1 years). Each subject performed sim-

ulative postural tremors four times with different amplitudes (no tremor, slight, moderate, and

severe). The glove monitoring system and Aurora EM system were running at the same time

during the experiments. The raw sensor data from the EM sensor and IMU were respectively

stored in the two laptops in real-time.

Figure 7-4: Setup for the verification of tremor quantification (Taken from Dai et al., 2013a). The test

subject performed “pill-rolling” action of the hand.

Figure 7-5: Raw signals measured with both the EM and tremor/bradykinesia assessment systems during

an imitated tremor task (Taken from Dai et al., 2013a). a) triple-axis EM localization signals (unit: mm);

b) IMU signals: triple-axis gyroscope signals and a one-axis combined acceleration signal (gyroscope unit:

°/s; accelerometer unit: g).

Figure 7-5 shows the ten-second signal waveform of both the EM sensor (localization signals)

and IMU (angular velocity and acceleration). According to Equation 5-4 in Chapter 5.3.1, the

acceleration readings in three axes were combined into an absolute linear acceleration and

then the gravitational acceleration was partly removed.

Results and Discussion

The parameters obtained from the tremor/bradykinesia assessment system were compared to

those calculated from the positional data, which were the outputs of the EM system, using the

IMU

EM sensor

EM generator

a)

b)

Experiments and Discussions

82

PSD estimation method. The peak powers, which is regarded as tremor amplitude, and domi-

nant frequencies from the two systems were compared.

The dominant frequencies between the two systems had little difference in the range from 2 to

6.5 Hz, even when the subjects did not perform a movement with stable tremor frequency.

As shown in Figure 7-6, for the postural tremor task, the dominant frequency of EM data is

plotted against the dominant frequency of the IMU signals from the tremor/bradykinesia as-

sessment system. The correlation coefficient (r) of dominant frequency between these two

systems was 0.996.

Figure 7-6: Correlation of the dominant frequencies in the imitated tremor tasks between the EM and

tremor/bradykinesia assessment system (unit of the dominant frequency: Hz). The frequencies were calcu-

lated using the PSD estimation method (Based on Dai et al., 2013a).

The maximum, mean, and standard deviation of the frequency differences between these two

systems were 0.570 Hz, 0.115 Hz, and 0.144 Hz respectively. Therefore, fmd (0.115±0.144

Hz) was smaller than 1.00±0.88 Hz.

A linear correlation between the two systems’ peak powers (tremor amplitude) could not be

shown. The reason for this is believed to be that seven subjects did not realize the imitated

tremor activity with a consistent amplitude and frequency during each timed task.

According to the experimental results, inconsistent movement made the peak power of the

EM signals smaller when using the PSD method. When some sampling points in the EM sig-

nals were missed, the peak power increased. These two points are important for the calcula-

tion of tremor amplitude.

For the two subjects who performed a stable movement (i.e., means consistent amplitude and

frequency as shown in Figure 7-5) the correlation between the two peak powers of the glove

monitoring system and the EM tracking system is shown in Figure 7-7. The peak power of the

glove monitoring system was calculated with both gyroscope and accelerometer signals using

the PSD method, while the peak power of EM signals was calculated only from the position

signals of the subject’s finger. Figure 7-7 shows that the correlation between these two sys-

tems was approximately linear. The correlation coefficient between these two systems

(r=0.97, p<0.001) was larger than 0.95.

0 1 2 3 4 5 6 70

1

2

3

4

5

6

7

Dominant frequnecy calculated from EM data

Do

min

an

t fr

eq

un

ecy c

alc

ula

ted f

rom

IM

U d

ata

Difference between frequencies:

ftb

(H

z)

fd

fem – ftb relation

0 1 2 3 4 5 6 70

1

2

3

4

5

6

7

Dominant frequnecy calculated from EM data

Do

min

an

t fr

eq

un

ecy c

alc

ula

ted

fro

m I

MU

da

ta

Difference between frequencies:

: fem (Hz)

Experiments and Discussions

83

Figure 7-7: Relationship of the tremor amplitudes (peak power) acquired with both the EM and trem-

or/bradykinesia assessment system (Based on Dai et al., 2013a). Two subjects each performed four imitat-

ed tremor tasks. The imitated tremor amplitudes varied in range from slight to severe for each subject.

For future research of the glove monitoring system, the RMS values of all sensor data should

be calculated. Together, the RMS values and peak powers could be used to determine the

tremor amplitude. The relation between the peak power of IMU signals and the consistency of

IMU signals in PD patients should be studied in the future.

The basic changes in the EMG or MER signals caused by PD symptoms are increased tonic

background activity and an alternating pattern of EMG or MER bursts. During the EMG or

MER analysis of PD symptoms, special attention has been paid to the analysis of these bursts

by measuring their counts, magnitudes, durations and frequencies (Rissanen et al., 2007). For

vigorous movement measurement, IMU provides good results. However, for slight motion

disorders, the difference between EMG and IMU requires further verification.

7.2 Verification of Hand Grasping Angle Calculation

Motivation

As the bradykinesia quantification is based on hand grasping angles, the hand grasping angle

calculation based on the integration of the triple-axis gyroscope signal is verified in this sec-

tion. Here the glove monitoring system refers to the tremor/bradykinesia assessment system.

Hypothesis

By comparing with those from the EM tracking system, the angular displacements and domi-

nant frequency of the hand grasps measured by the glove monitoring system should meet the

following requirements:

Mean value and standard deviation of the differences of dominant frequencies: fmd <

1.00±0.88 Hz (Niazmand et al., 2011b).

Mean difference of the angular ranges of hand grasps: md < 0.1 · em.

Here em is the mean angular range of hand grasps (peak-to-peak value) measured by the

EM tracking system.

Materials

An NDI Aurora®

EM tracking system with a six-DOF sensor (Northern Digital Inc.,

Canada)

-10 0 10 20 30 40 50 60-15

-10

-5

0

5

10

Peak Power from EM data: (mm2/Hz)

Norm

aliz

ed P

eak P

ow

er

fr

om

IM

U d

ata

:

E

R

Experiments and Discussions

84

A tremor/bradykinesia assessment system (included a JTAG USB cable and a com-

mand module with a sensor board)

A laptop with the application software of the EM system (Aurora Toolbox)

A laptop with the application software of the tremor/bradykinesia assessment system

(LabVIEW-based GUI for tremor/bradykinesia assessment; MATLAB R2008b for

data analysis)

Parameters

Difference of the dominant frequencies from both the EM tracking system and the

tremor/bradykinesia assessment system

Mean difference of the hand grasping ranges between the EM tracking system and

tremor/bradykinesia assessment system during a ten-second hand grasping task

The standard deviation value of the differences of the hand grasping ranges from both

the EM tracking system and tremor/bradykinesia assessment system during a ten-

second hand grasping task

Methods

The dominant frequencies of hand grasps calculated by the tremor/bradykinesia assessment

system and the EM tracking system were ftb and fem respectively (see Chapter 5.3.2). Then the

difference of their dominant frequencies was:

. (7-4)

As described in Equation 7-2, fmd (mean and standard deviation of frequency differences)

could be calculated with the values of fd during all bradykinesia assessment tasks.

The mean angular range of hand grasps calculated by the tremor/bradykinesia system and the

EM tracking system were tb and em respectively (see Chapter 5.3.2). Then the difference

of their mean angular ranges of hand grasps during a single bradykinesia task was:

. (7-5)

Therefore, md could be calculated with the values of d during all bradykinesia assessment

tasks:

, (7-6)

where N was the amount of imitated bradykinesia assessment tasks (N=18 in this experiment).

Experiment Setup

As Figure 7-8 shows, the tremor/bradykinesia assessment system and the EM tracking system

were used to measure the hand grasps by nine healthy subjects (average age: 29.0±2.1 years).

Each test subject performed such tasks three times. The test subject’s hand was in the meas-

urement volume of the EM tracking system. A six-axis EM sensor and a sensor board (IMU)

were attached to the middle finger.

Experiments and Discussions

85

Figure 7-8: Setup for the verification of the bradykinesia quantification method.

Results and Discussion

As shown in Figure 5-14 in Chapter 5.3.2, only the gyroscope signal was used to calculate the

bradykinesia parameters. Thus, the errors in the calculated grasping angles were mainly com-

posed of the gyroscope drift and integration error. To reduce the errors in a short period of

time, the gyroscope was calibrated upon system initiation. Figure 7-9 shows the raw data of

the EM tracking system and the glove monitoring system for a ten-second bradykinesia task.

Figure 7-9: Raw sensor data both from the EM tracking system (location and orientation data) and the

glove monitoring system (angular velocity) during the one-time bradykinesia task of a subject (Taken

from Dai et al., 2013a).

According to the analysis of the sensor signals of this experiment, three subjects performed

hand grasping movements with different frequencies. Therefore, their data were removed.

The EM signals (three-dimensional orientation data) were processed using the FFT method to

get the dominant frequency and the peak-detection method to obtain grasping ranges. The

IMU signals (angular velocity) were processed using the methods described in Chapter 5.3.2.

Table 7-1 shows the differences of bradykinesia parameters between the EM tracking system

EM sensor & IMU

αxa

ya

za

0 1 2 3 4 5 6 7 8 9 10-500

0

500

IMU

angula

r velo

city

(

degre

e/s

)

αza

yaxa

Time (s)

100

0

-1005

-100

10100

0

0 1 2 3 4 5 6 7 8 9 10

0 1 2 3 4 5 6 7 8 9 10

500

0

-500

EM

orie

nta

tio

n

(de

gre

e)

EM

lo

ca

tio

n

(mm

)

IMU

an

gu

lar

ve

locity

(de

gre

es/s

)

Experiments and Discussions

86

and the IMU-based glove monitoring system (six test subjects, each performed the task three

times).

Table 7-1: Absolute differences of parameters between the EM tracking system and the IMU-based

glove monitoring system (bradykinesia task)

Parameters Frequency (Hz) Mean range: SD of ranges:

Max. Difference 0.70 22.18° 7.43°

Mean Difference 0.18 17.60° 3.65°

SD Difference 0.30 6.24° 3.14°

The difference of dominant frequencies (fmd) between the glove monitoring system and the

EM tracking system (0.18±0.30 Hz) was smaller than 1.00±0.88 Hz.

The angular ranges of the finger movements were more than 200° in a single cycle during this

experiment. Thus the difference in mean range between these two systems ( md =17.60°)

was smaller than 10% of the mean angular ranges ( em>200°) of the finger movement dur-

ing the bradykinesia task.

As there were some bad fits in the results of the EM tracking system during hand grasping

movements, another tracking system, such as an optical tracking system, can be used as the

reference system in the future.

In order to improve the accuracy of the bradykinesia parameters, further signal processing

methods must be carried out. The integration method of angular velocity plays a key role in

the calculation of bradykinesia parameters. Also a modified DCM algorithm can be used in

calculating the hand-grasping angle ranges.

7.3 Verification of Analytical Methods for Rigidity Assessment

7.3.1 Impact of Eccentric Application of Force on Force Sensor Box Outputs

Motivation

FSRs have a nonlinear characteristic. Therefore, in the situation that force is introduced out-

side the middle of the FSR’s active area, the result is a lower output than when force is direct-

ly applied to the middle of the active area.

Similarly, when the applied force is performed more towards the edges of the force sensor

box, the result is a lower output than when force is applied to the middle of the box. However,

the magnitude of this effect remains unclear since the characteristics vary among FSRs and

also depend on the chosen contact pad setup.

An experiment was carried out to obtain the deviation between the calculated force value and

its real value (weight value) when an examiner pressed different parts of the force sensor box-

es.

Hypothesis

Experiments and Discussions

87

According to the datasheet of FSR149NS, its accuracy ranges from 5% to 25%. Therefore, the

following requirements should be met:

Accuracy (relative deviation): Val < 25%.

Materials

A series of iron weight plates (2503000 grams)

A rigidity assessment system (included a rigidity cuff and a USB cable)

A laptop with the application software of the rigidity assessment (Visual C# 2010-

based GUI for rigidity assessment and MATLAB R2008b for data analysis)

Parameters

The difference between the measured value and the attached weight load was defined as: Val

Methods

The relative deviations were calculated according to the following formula:

– , (7-7)

where F(out) was the measured force value and F(weight) was the weight value attached to

the force sensor box. The unit of F(weight) was also gram.

The mean absolute value of the relative deviations at a certain point or for a certain weight

was expressed as:

, (7-8)

where N was the number of values at a certain point or for a certain weight in different points.

Experiment Setup

Figure 7-10: Plot with depressed points on force sensor box 1, in which X0 is the central point of the press-

ing area. These same points were marked on the force sensor box 2 as well (© TUM-MIMED, 2012).

As Figure 7-10 shows, four FSR sensors were located underneath the points marked with X11,

X22, X33, and X44. Points X1, X2, X3, and X4 were located at the midways between the center

X0 and X11, X22, X33, and X44, respectively.

Similarly to the calibration of force sensor boxes in Chapter 7.1.2, a series of weight loads

were applied to the force sensor boxes. However, in this experiment, the force was not only

applied to the central point of the force sensor boxes but also to the edges. Ten weight loads

ranging from 250 grams to 3000 grams were exerted on the marked points, shown in Figure

Experiments and Discussions

88

7-10, of each force sensor box. As a result, 90 voltage values for each force sensor box were

acquired.

Derivation Values of the Measurements in Different Points

Table 7-1: Relative deviations of force sensor box 1 and the mean absolute values of the relative devi-

ations. The applied points with the biggest relative deviation are highlighted.

Force

[N]

Relative deviation

X1 X11 X2 X22 X3 X33 X4 X44 X0

2.47 14% 49% 39% 47% 14% -3% -38% -59% 8% 30%

3.45 32% 35% 29% 22% 8% -6% -35% -70% -1% 27%

6.29 19% 29% 17% 19% 7% -15% -39% -74% -1% 24%

5.10 24% 34% 23% 9% 8% -10% -45% -72% 12% 26%

9.90 13% 15% 19% -11% 1% -24% -33% -73% 7% 22%

14.74 6% 8% 9% -5% -16% -37% -41% -73% 5% 22%

19.68 0% -9% 3% -9% -23% -41% -32% -77% 5% 22%

24.57 -10% -12% 1% -19% -31% -53% -33% -53% -2% 24%

38.55 -17% -37% -7% -34% -45% -48% -40% -62% -11% 33%

30.05 -11% -22% -3% -10% -34% -43% -36% -59% -12% 26%

15% 25% 15% 19% 19% 28% 37% 67% 6%

Table 7-2: Relative deviations of force sensor box 2 and the mean absolute values of the relative de-

viations. The applied points with the biggest relative deviation are highlighted.

Force

[N]

Relative deviation

X1 X11 X2 X22 X3 X33 X4 X44 X0

1.49 15% -6% 8% -5% -1% -16% 2% -11% 5% 8%

2.47 4% -10% 5% -10% -6% -24% 17% -14% 13% 11%

3.45 5% -14% 8% 4% -4% -12% 10% -17% 13% 10%

6.29 6% -11% 6% 2% -6% -9% 6% -32% 7% 9%

5.10 3% -12% 9% 15% 6% -6% 11% -14% 8% 9%

9.90 2% 2% -1% -17% -13% -17% -1% -19% 14% 10%

14.74 3% -1% -10% -9% -13% -25% -11% -8% 8% 10%

19.68 4% 1% -14% -14% -29% -33% -19% -41% 2% 17%

24.57 -3% -14% -23% -28% -25% -41% -31% -42% 1% 23%

5% 8% 9% 12% 11% 20% 12% 22% 8%

In the first step, the recorded sensor output was normalized to values of Gramm using Equa-

Experiments and Discussions

89

tion 6-1. Table 7-1 and Table 7-2 show the relative deviations which were calculated accord-

ing to the Equations 7-7 and 7-8.

When the weights, ranging from 2.47 to 30.05 N, were applied to the center of both force sen-

sor boxes ten times, the average deviations of two force sensor boxes were 6% and 8%, re-

spectively.

Summary

When force was applied to the center of the force sensor boxes, the relative deviations were

moderate ( <15%). However, if the force was applied to the edges of the force sensor

boxes, two effects, which impaired the output, could be observed. Firstly, values measured

when applying force more to the edges of the sensor box tended to be lower than when force

was applied to the center. This was likely due to the nonlinear characteristics of the sensors.

Secondly, when force was applied to the edges, the underlying sensor beared a greater share

of the total force and was already closer to saturation than when it was during centric applica-

tion of force.

However, this effect seems to be influenced by the differences in sensitivity of FSRs. Even

though the FSRs had been selected to match in terms of characteristics, deviations depended

strongly on which of the force sensor was exposed to the greatest share of total load. As it can

be seen in column X44 of Table 7-1 (highlighted), the FSR 4 in force sensor box 1 had a very

low sensitivity compared to the other FSRs and therefore influenced the results.

In order to avoid such problem, it is necessary to either select the FSRs more carefully or

connect each FSR in the force sensor boxes to a single analog input (ADC) pin of the micro-

controller and perform individual calibration of each single FSR in the two force sensor

boxes.

In addition, the weight values ranged only from 2.47 to 30.05 N. A larger weight range

(3080 N) should be verified in the future research.

7.3.2 Verification of Wrist Angle Calculation

Motivation

As the torque of elbow passive movement is based on wrist angular displacement, the verifi-

cation of the elbow angle calculation is introduced in this section. The examiner can hold the

patient’s elbow to perform straight movement along a single axis, and then only the move-

ment around the EM generator’s Z-axis was verified in this experiment.

Hypothesis

The calculation of the elbow’s angular displacement during passive movement was based on

the sensor fusion (see Chapter 5.3.2). By comparing with those from the EM system, the an-

gular displacements of the elbow movement measured by the glove monitoring system should

meet the following requirement:

Mean difference of the angular displacements of the two systems during all tested ri-

gidity tasks (elbow movement)

α mz < 0.1 · emz.

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90

Here α emz is the mean angular range of the ten-second elbow movement (peak-to-peak value

in Z-axis) measured by the EM tracking system.

Materials

An NDI Aurora®

EM tracking system with a six-DOF sensor (Northern Digital Inc.,

Canada)

A rigidity assessment system (included a rigidity cuff and a USB cable)

A computer installed with MATLAB R2008b (Mathworks Inc., USA), EM application

software (Northern Digital Inc., Canada), and rigidity assessment software (Visual C#

2010, Microsoft Inc., USA)

Parameters

Maximum difference of the calculated elbow angles from both the EM tracking sys-

tem and the rigidity assessment system during all the ten-second rigidity assessment

tasks

Mean difference of the calculated elbow angles from both the EM tracking system and

the rigidity assessment system during all the ten-second rigidity assessment tasks

Standard deviation of the differences of the calculated elbow angles from both the EM

tracking system and the rigidity assessment system during all the ten-second rigidity

assessment task

Methods

Z-axis elbow angle at a sampled point of the EM tracking system was , which was ac-

quired directly from the NDI EM tracking system. At the same time, the Z axis elbow angle

measured by the rigidity assessment system was , which was calculated using the six-axis

DCM algorithm based on the IMU signals (see Chapter 5.3.2).

Therefore, the difference of angular displacements in a sampled point was:

. (7-9)

The mean difference of the angular displacements during all the ten-second elbow movements

was:

α mz , (7-10)

where N was the number of all sampled points. For one time rigidity assessment, the sampled

points were 1000. There were five valid measurements in this experiment. Thus N was 5000.

The calculation of emz is the same as the method described in Chapter 5.3.2.

Setup

Only one test subject (age: 27 years) was involved in this experiment. He performed the rigid-

ity task six times. As shown in Figure 7-11, a six-DOF EM sensor attached to the wrist was

used in the elbow-motion-tracking experiment. With the help of an examiner, the subject per-

Experiments and Discussions

91

formed elbow flexion and extension movements around the Z axis of the EM generator, while

the end of the elbow acted as a fulcrum.

Figure 7-11: Setup for the verification of elbow angle calculation. The subject performed the same elbow

movement (around the Z-axis) during the rigidity task. An EM sensor was attached to the wrist (Based on

Dai et al., 2013a).

The elbow’s angle values, which were obtained from the EM system directly, were compared

to the elbow angles calculated using the DCM method with the IMU data.

Results

Figure 7-12 shows the angular displacements of elbow’s movement both from the IMU based

rigidity assessment system (DCM fusion algorithm) and from the EM tracking system in the

second measurement. Their difference and the maximum difference over a ten-second task are

also shown in Figure 7-12.

Figure 7-12: Plot of DCM fusion (IMU tracking) versus EM tracking. The elbow movement during a ri-

gidity assessment task was measured with both IMU and EM sensors. IMU signals were processed using

the DCM algorithm. The EM tracking values (angles) were directly obtained from the EM tracking sys-

tem (Based on Dai et al., 2013a).

In the sensor data of the sixth measurement, too many sampling points from the EM data were

missing because of an error in the NDI toolbox; thus only five measurements were analyzed.

Table 7-3 shows the absolute differences between the EM tracking system and the rigidity

assessment system from all the five measurements.

Rigidity cuff

EM sensors

Experiments and Discussions

92

Table 7-3: Absolute differences between the EM tracking system and the IMU based rigidity assess-

ment system

Test Number 1 2 3 4 5

Max. Difference 9.41° 8.27° 12.50° 9.34° 9.90°

Mean Difference 4.40° 4.90° 4.86° 4.70° 4.87°

SD Difference 1.69° 1.82° 2.01° 1.96° 2.05°

The maximum, mean value, and standard deviation of the differences between these two sys-

tems in all test subjects were 12.50°, -4.74°, and 1.91° respectively. The angular ranges of

elbow movements were about 80° during this experiment. Thus the mean difference ( α mz =

-4.74±1.91°) was smaller than 10% of the elbow angular range ( emz >80°).

7.3.3 Verification of Mechanical Impedance Components

Motivation

In order to acquire the correlation between the UPDRS ratings and mechanical impedance,

first the correlation of elastic stiffness and viscosity with the rigidity severity should be inves-

tigated. In the presented experiment, the rigidity assessment algorithm was verified.

Hypothesis

For Cohen's d, an effect size of 0.8 to infinity denotes a “large” effect (Nakagawa et al.,

2007). Therefore, the following requirements should be met:

Effect size d > 0.8.

p-value < 0.05.

Materials

A rigidity assessment system which includes a rigidity cuff and a USB cable

A computer installed with MATLAB R2008b and rigidity assessment software (Visual

C# 2010)

A wooden folding ruler in hard wood (scale range: 1 m)

Parameters and Methods

To assess if the proposed system was able to detect an increase in mechanical rigidity at a

significant level, the four sample reference data were t-tested against the four sample data for

every test subject. Hence a one-side paired t-test between reference state (relaxed) and imitat-

ed rigid state of each volunteer was performed. The difference between the elasticity or vis-

cosity between the relaxed state and imitated rigidity state was described as:

, (7-11)

where

Experiments and Discussions

93

Y=Elasticity (or viscosity) while the test subjects contracted arm muscles;

Z=Elasticity (or viscosity) while the test subjects kept muscles relaxed.

The calculations of elasticity and viscosity are described in Chapter 5.3.3.

The standard deviation, t-test formula, and effect size of X are described as Equations 7-12, 7-

13, and 7-14, respectively.

Standard deviation of X:

N

i

i XXN

XS1

)(1

; (7-12)

One-side paired t-test: NXS

Xt ; (7-13)

Cohen’s XSXd ; (7-14)

where S(x) was the standard deviation of X, N was the number of measurements, and was

the average value of X.

Experimental Setup

As Figure 7-13 shows, nine healthy volunteers (average age: 24.4±4.2 years) were tested with

the rigidity assessment system, yielding eight measurements each. During the first four refer-

ence movements, the volunteers were asked to relax (i.e., with no rigidity), while in the next

four movements they were asked to imitate rigidity.

Figure 7-13: Experiment with the rigidity assessment system (Based on Dai et al., 2013b).

Table 7-4: Ranges and frequencies of four times elbow movements for a test subject during the rigidity

assessment tasks

Number Range Frequency

1 60° 0.5 Hz

2 60° 1.0 Hz

3 120° 0.5 Hz

4 120° 1.0 Hz

Experiments and Discussions

94

Also, during the assessments, the examiner tried to perform different elbow movement ranges

and frequencies according to Table 7-4.

Results

Figure 7-14 shows the torque-displacement plots both in normal condition (i.e., relaxed state)

and imitated rigidity condition.

a) b)

Figure 7-14: Torque-displacement plots (Taken from Dai et al., 2013b). a) normal condition (relaxed),

elbow movement range: 100°, dominant frequency: 1.2 Hz; b) imitated rigidity condition, elbow move-

ment range: 120°, dominant frequency: 1.1 Hz.

The viscosity values, elasticity values, and the frequencies during this experiment were calcu-

lated with the algorithms stated in Chapter 5.3.3.

The viscosity and elasticity were converted to absolute values before calculation. The average

value of viscous modulus with no rigidity (i.e., relaxed state) was 0.26±0.08 N·m/degree, and

0.78±0.45 N·m/degree in the imitated rigidity state. The average value of elastic modulus

with no rigidity (i.e., relaxed state) was 0.99±0.53 N·m/degree, and 3.78±2.85 N·m/degree in

the imitated rigidity state.

Table 7-5: Evaluation of the viscosity. The insignificant value (p>0.05) is highlighted with yellow.

Subject 1 2 3 4 5 6 7 8 9 Mean

value

7.1 12.3 5.4 7.8 12.1 8.1 3.1 4.0 4.8 4.81

S(X) 3.7 6.3 1.45 3.7 2.8 3.6 2.9 1.2 1.4 1.35

Effect

size (d) 1.91 1.94 3.74 2.13 4.40 2.26 1.07 1.35 3.55 1.65

p-value 0.015 0.015 0.002 0.012 0.002 0.01 0.061 0.036 0.003 <<

0.001

Table 7-5 and Table 7-6 show the viscosity and elasticity of the subjects assessed by the rigid-

ity assessment system, respectively.

Experiments and Discussions

95

Table 7-6: Evaluation of the elasticity. The insignificant value (p>0.05) is highlighted with yellow,

while the negative elasticity is highlighted with green.

Subject 1 2 3 4 5 6 7 8 9 Mean

value

0.94 1.00 0.54 0.80 2.39 0.14 0.58 -0.26 0.65 0.75

S(X) 0.44 0.21 0.27 0.12 0.71 0.13 0.23 0.12 0.13 0.75

Effect

size (d) 2.11 4.78 1.99 6.77 3.34 1.04 2.43 -2.20 4.81 1.00

p-value 0.012 0.001 0.014 <0.001 0.003 0.065 0.008 - 0.001 <<0.001

For eight of the nine test subjects, the proposed system was able to detect a significant change

in viscosity (p<0.05). For eight of the nine test subjects, a significant change of the elbow

joint elasticity (p<0.05) was also detected. The effect size (Cohen's d) of viscosity and elastic-

ity between normal state and imitated rigidity were therefore, “large”, at 1.61 and 1.36

(d>0.8), respectively.

Influence of Angular Range and Frequency of the Elbow Movement

The viscosity and elasticity versus elbow ranges and frequencies are shown in Figure 7-15 to

Figure 7-18, which were produced using the freely available statistical software package R

(version 2.15.2).

Figure 7-15: Plot of viscosity against the range of passive elbow movement (© TUM-MIMED, 2012).

Angular range [º]

Vis

co

sity [

N·m

·s/º

]

Measured value while the test

performed imitated rigidity

Measured value while the test

was in relaxed state

Experiments and Discussions

96

Figure 7-16: Plot of elasticity versus the range of passive elbow movement (© TUM-MIMED, 2012).

Figure 7-17: Plot of viscosity over the frequency of passive elbow movement (© TUM-MIMED, 2012).

Frequency [Hz]

Ela

sticity

[N·m

/º]

Angular range [º]

Vis

co

sity [

N·m

·s/º

]

Measured value while the test

performed imitated rigidity

Measured value while the test

was in relaxed state

Measured value while the test

performed imitated rigidity

Measured value while the test

was in relaxed state

Experiments and Discussions

97

Figure 7-18: Plot of elasticity over the frequency of passive elbow movement (© TUM-MIMED, 2012).

The result shows that the frequencies and ranges of these passive elbow movements were not

exactly the same as the settings in Table 7-7, because it was very difficult for the examiner to

keep accurate movement frequency or range in an assessment task. The mean and SD values

of viscous modulus and elastic modulus in different movement ranges and frequencies are

displayed in Table 7-7.

Table 7-7: Mean and standard deviation of the absolute viscosity and elasticity

Range Frequency

60° 120° 0.5 Hz 1 Hz

Viscosity

(normal) 0.28±0.08 0.23±0.09 0.25±0.08 0.26±0.09

Viscosity

(rigid) 0.79±0.46 0.77±0.44 0.80±0.55 0.76±0.39

Elasticity

(normal) 1.15±0.83 0.82±0.51 0.81±0.51 1.18±0.62

Elasticity

(rigid) 3.55±3.43 4.01±2.47 3.55±2.95 4.02±2.96

The result shows that the frequency and range of elbow movement had little effect on the vis-

cosity. In contrast, movement frequency had a greater effect on the elasticity, which might

have negative influences when the examiner flexed and extended the forearm at different

speeds. As a result, if the neurologist wants to obtain the mechanical impedance according to

the formula of mechanical impedance (see Chapter 5.3.3), it is important to keep the same

frequency. Compared with normal state (relaxed), elasticity varies largely in the imitated ri-

gidity state.

Frequency [Hz]

Ela

sticity [

N·m

/º]

Measured value while the test

performed imitated rigidity

Measured value while the test

was in relaxed state

Experiments and Discussions

98

According to the formula of mechanical impedance, elasticity depends on the movement fre-

quency and range, and other factors. Thus, the mean value of elastic modulus had a large

standard deviation. Another reason is that the test subjects did not perform imitated rigidity in

the same state, which means the imitated rigidity varied.

7.4 Experiment of Tremor Assessment

Clinical experiments of patients with tremors were carried out with the glove monitoring sys-

tem. The glove monitoring system in this section refers to the tremor/bradykinesia assessment

system (prototype for tremor and bradykinesia assessments).

Hypothesis

The tremor amplitude correlation between the glove monitoring system and the clinical rat-

ings should meet the requirement:

Correlation coefficient r > 0.84 (Giuffrida et al., 2009).

Materials

A tremor/bradykinesia assessment system (included a command module with a sensor

board, and a JTAG USB cable)

A computer installed with MATLAB R2008b (MathWorks Inc., USA) and the GUI

(LabVIEW 2010 Evaluation Version, National Instruments Corp., USA) of the

tremor/bradykinesia assessment system.

Parameters and Evaluation Methods

As described in Section 5.3.1, the tremor amplitude calculated by the glove monitoring sys-

tem was R, while the tremor amplitude judged by the surgeon was D. Then the correlation

coefficient between these two parameters was:

, (7-15)

where N is the amount of assessed patients.

For the valid state detection (see Chapter 5.3.1), only the gyroscope signal was utilized. Pa-

rameters for the valid state detection both in the frequency domain and time domain were:

; (7-16)

, (7-17)

where was the matrix of peak-to-peak values of the triple-axis gyroscope signals during

a ten-second tremor task, peak power was the power estimation around the dominant tremor

frequency (±0.3 Hz).

Experiment Setup

A total of five patients with tremors were tested with this wearable system. The tremor severi-

Experiments and Discussions

99

ty of these patients ranged from 1 to 3 (UPDRS tremor scores: D). The measurements in these

patients were performed after stopping their medication for more than 24 hours.

Rest, postural, and action tremor assessment tasks were performed. However, for each patient,

only the hand side with a more severe tremor was assessed. Each tremor assessment task last-

ed for ten seconds.

The tremor amplitude was represented the tremor severity in this system. Time-frequency

analyses and statistical analyses were carried out based on the IMU signals. The valid state

detection algorithm was performed for each assessment task.

As shown in Figure 7-19, a PD patient performed three tremor assessment tasks in turn ac-

cording to the instructions of a surgeon.

a) b) c)

Figure 7-19: Experiments with the tremor/bradykinesia assessment system. a) rest tremor; b) postural

tremor; c) action tremor.

Each patient performed 3–5 times each task repeatedly. Each task lasted ten seconds.

Results

Figure 7-20 shows the IMU signals of the glove monitoring system and their power spectra

for a PD patient with slight rest tremor (D=1). In Figure 7-20 (a), the tremor occurred only for

several seconds and with varying amplitude. In Figure 7-20 (b), there was only a small ampli-

tude fluctuation during the ten-second period.

The tremor amplitude calculation with PSD method depends on the precondition that the

tremor movement is stable and the power estimation around the dominant frequency is sharp.

Therefore, the signals in Figure 7-20 (b) were able to be used to calculate the tremor ampli-

tude. The assessment tasks which had bigger tremor amplitude fluctuation than Figure 7-20

(b) were discarded during signal processing because the tremors were not in a stable condi-

tion. Although Heldman et al. (2011b) indicated that the tremor amplitude calculated with

PSD correlated well with clinical ratings even for a broad power spectra distribution, the

power distribution in other frequency points still influenced the tremor amplitude calculation

in these measurements.

As shown in Figure 7-20, (d) is the normal situation in frequency domain while (c) is not

good for tremor calculation with the PSD method (Timmer et al., 1997).

For the action tremor assessment, it was difficult to separate the active movement with tremor

even with different band-pass filters. Its power spectrum included multiple frequency compo-

nents. A better signal filter should be adopted in the future (Timmer, et al. 1993).

Experiments and Discussions

100

When performing the signal processing algorithms, the rest and postural tremor tasks were

assessed as valid state or invalid state for PSD estimation. The amplitude of action tremor was

not included in the present results.

a) invalid state in time domain (Vt = 0.64);

b) valid state in time domain (Vt = 0.23);

c) invalid state in frequency domain (Vf = 69.4%); d) valid state in frequency domain (Vf = 91.3%).

Figure 7-20: Tremor state of a PD patient (UPDRS tremor score: D=1). a) and b): waveforms of triple-axis

raw accelerations and triple-axis angular velocities (rest tremor); c) and d): combined power spectra of

the triple-axis acceleration signals (bottom chart) and the combined power spectra of the angular velocity

signals (upper chart). b) and d) are for the same tremor assessment task.

In order to investigate the correlation between the inertial sensor outputs and clinical ratings,

the outputs of the gyroscope and the accelerometer were analyzed independently as well. As

shown in Table 7-8, the parameters, calculated with the sensor signals, were compared to the

judgments of a surgeon according to the UPDRS and TETRAS ratings. These results were

from the average values of valid rest and postural tremor tasks.

Table 7-8 shows that the gyroscope signal had a stronger correlation (r=0.92) with the clinical

scores than the accelerometer signal (r =0.81).

Both the RMS value of the gyroscope signal and peak power from all IMU outputs have the

biggest correlation with the clinical scores (r=0.92). In the program of the glove monitoring

system, the frequency range of peak power was 0.6 Hz. Thus, the frequency range should be

wider in the next version of this glove monitoring system.

Frequency (Hz) Frequency (Hz)

Experiments and Discussions

101

Table 7-8: Results for rest and postural tremor assessments. Here acc. represents accelerometer; gyro.

means gyroscope; RMS means root-mean-square; ln means natural logarithm; R is the predicated

tremor score from the glove monitoring system; r denotes the correlation coefficient between the rela-

tive parameters calculated by the IMU signals and clinical scores judged by the surgeon.

It can also be seen from Table 7-8 that the proportion of rotational movement and translation

movement of a tremor [ln(gyro. Power) versus ln(acc. Power)] varies from patient to patient.

More tremor measurements are needed to acquire further symptom information and better

regression coefficients.

Figure 7-21 shows the dominant tremor frequencies of a patient during three times rest tremor

tasks (numbers 1 to 3) and three times postural tremor tasks (numbers 4 to 6). The difference

of dominant frequencies from gyroscope and accelerometer was small (<0.2 Hz). For all the

measured patients, the dominant tremor frequency was constant or with small fluctuations

even if the tremor amplitude changed during ten-second task period.

Figure 7-21: Dominant tremor frequency of a PD patient (UPDRS tremor score D=1) during the assess-

ment tasks of rest tremor (numbers 1 to 3) and postural tremor (numbers 4 to 6). The dominant tremor

frequencies were calculated from accelerometer signals and gyroscope signals, respectively.

Discussion

Results indicate that the tremor frequency was stable for all patients; however, the tremor am-

plitude fluctuated all the time. The quantitative assessment of tremor, with an IMU and adap-

tive algorithms, provided an objective rating to classify rest or postural tremor severity even

with a small scale.

Experiments and Discussions

102

7.5 Experiment of Bradykinesia Assessment

Motivation

For the measurements in a healthy subject or a patient with mild bradykinesia, the signals

obtained from the gyroscope have a consistent amplitude and frequency and appear sinusoi-

dal. However, the signals from a patient with severe bradykinesia have a much lower and in-

consistent amplitude and frequency.

Thus the mean and standard deviation of amplitude and frequency during bradykinesia meas-

urement tasks represent the parameters of bradykinesia.

The prototype should be tested both with healthy volunteers and PD patients to demonstrate a

difference between the PD patients and healthy subjects. The glove monitoring system in this

section refers to the tremor/bradykinesia assessment system.

Hypothesis

The correlations of bradykinesia parameters between the glove monitoring system and clinical

ratings should meet the following requirement:

Correlation coefficient r > 0.79 (Giuffrida et al., 2009).

Parameters and Evaluation Methods

As described in Section 5.3.2, the bradykinesia parameter calculated by the glove monitoring

system is R, while the severity of bradykinesia judged by the surgeon is D. Then the correla-

tion coefficient between these two parameters is

, (7-18)

where N is the amount of assessed patients.

Along with dominant frequency of hand grasping, mean and SD values of hand grasping

ranges ( ) were regarded as bradykinesia parameters. In addition, we defined the product of

dominant frequency and mean range as the modified mean range.

Materials

A tremor/bradykinesia assessment system which included a JTAG USB cable and a

command module with a sensor board

A computer installed with the GUI of the tremor/bradykinesia assessment system

(LabVIEW 2010 Evaluation Version, National Instruments Corp., USA) and MAT-

LAB R2008b (Mathworks Inc., USA)

Experiment Setup

Three healthy volunteers (average age: 49.0±16.6 years) and five PD patients (average age:

75.5±11.1 years) should execute hand grasping movement as quickly and widely as possible

for ten seconds, and repeat the assessment task three times for each subject. However, a se-

vere PD patient (UPDRS bradykinesia score D=4) was unable to perform the bradykinesia

Experiments and Discussions

103

task.

One time grasping movement included several grasp cycles, which was the number of peak-

to-peak ranges.

An IMU, which was attached to the middle finger, was used to measure the angular displace-

ment of the middle finger movement during the bradykinesia assessment task. The dominant

grasping frequency, mean, and standard deviation of hand grasping ranges were used as the

severity features of bradykinesia.

The bradykinesia parameters, calculated by the glove monitoring system, were compared to

the ratings of a surgeon.

Results and Discussion

Figure 7-22 shows three ten-second waveforms of hand grasps and their PSD figures. For

some patients, as shown in Figure 7-22 (b), action tremor appeared during the bradykinesia

assessment task.

Figure 7-22: Ten-second hand grasps and their PSD figures. a) healthy subject, age: 72 years; b) PD pa-

tient with tremor and bradykinesia, age: 82 years, UPDRS bradykinesia score D= 1; c) PD patient, age: 86

years, UPDRS bradykinesia score D= 3. The peak powers in the figure were calculated on a weighted

scale.

In addition, as shown in Figure 7-22 (c), there was a delay time for a patient with severe

bradykinesia to start the hand grasping movement after the instruction from the examiner.

This situation is the symptom of akinesia (difficulty initiating movement).

The three healthy subjects had a dominant frequency of 1.16 Hz and a standard deviation of

0.11 Hz. The parameter values are presented in Table 7-9. Table 7-9 shows that the patients

with severe bradykinesia executed grasping movements with lower dominant frequencies.

As Table 7-9 shows, the dominant frequency of hand grasps and the modified mean range had

the higher correlations with the UPDRS score (r=-0.94 and -0.86 respectively).

0 2 4 6 8 10

0

100

-100

0

200

-200

0

150

-100

Time (s)

0 2 4 6 8 10

0 2 4 6 8 10 0 2 4 6

0 2 4 6

0 5 10

Frequency (Hz)

5

10

0

2

4

0

2

4

0

Angle

( )

Angle

( )

Angle

( )

Peak p

ow

er

Peak p

ow

er

Peak p

ow

er

a)

b)

c)

Combined angle of hand grasps ( )α PSD figure of grasping angles

Experiments and Discussions

104

Table 7-9: Results of bradykinesia assessment tasks. Freq. represents frequency; r denotes the correla-

tion coefficient between the relative parameters and clinical scores.

Subjects Dominant Freq. Mean Range SD Ranges Modified

Mean Range

UPDRS

Score (D)

Healthy 1 1.09 Hz 202.3° 10.6° 220.5 Hz·° 0

Healthy 2 1.29 Hz 255.4° 11.5° 329.5 Hz·° 0

Healthy 3 1.10 Hz 289.8° 19.8° 318.8 Hz·° 0

Patient 1 0.88 Hz 241.6° 10.2° 212.6 Hz·° 1

Patient 2 0.60 Hz 181.7° 9.47° 109.0 Hz·° 1

Patient 3 0.44 Hz 212.8° 21.0° 93.6 Hz·° 2

Patient 4 0.32 Hz 221.8° 3.6° 71.0 Hz·° 3

r -0.94 -0.36 -0.32 -0.86

There were also subjects, such as Healthy 1 and Patient 1, whose mean grasping range deviat-

ed from the normal situation. The reason may be the age of subjects. The healthy subject was

too old to reach great amplitudes during hand grasping movements. On the other hand, the PD

patient with a smaller age was able to grasp at a higher amplitude. Therefore, the obtained

parameters need to be modified according to the subject’s age. For the SD values of the grasp-

ing ranges, there was only a small correlation coefficient (r=-0.32) to the UPDRS ratings in

this experiment.

As a conclusion, the difference of the parameters between the healthy subjects and PD pa-

tients was significant. Further clinical measurements should be performed in the future.

7.6 Conclusion

As a force sensor box of the rigidity assessment system had four FSRs and the force-

resistance characteristic was nonlinear, a two-term Gaussian regression function was used for

each force sensor box calibration.

A better cognition of the analytical methods in this system was achieved with these verifica-

tion results, providing the necessary science and engineering to guide future signal processing

methods. Then the clinical experiments could be performed.

The verification experiment of tremor amplitude and frequency indicates that the dominant

tremor frequencies from both systems had little difference (0.115±0.144 Hz). For the imitated

tremor task with consistent movement, the calculated tremor amplitude using the IMU based

system correlated well with the tremor amplitude from the EM system (r=0.97). The results of

clinical experiment indicate that the tremor amplitude calculation with the PSD method was

valid only in the constant state. Thus the tremor tasks were divided into valid state and invalid

state. Rest and postural tremors in valid state were correlated with the UPDRS scores

(r=0.92). For the action tremor amplitude calculation, quadratic mean method or other algo-

rithms should be employed. The dominant tremor frequency of a patient for one type of trem-

or task had small fluctuations even in an invalid tremor state (r=0.996) (Elble et al., 2006).

Experiments and Discussions

105

Further measurements are needed to modify the regression coefficients in the regression func-

tion for tremor calculation.

The difference between parkinsonian tremor and ET needs to be investigated.

The analysis as to whether there are single or multiple dominant frequencies during tremor

assessment tasks also needs to be performed (Mostile et al., 2010).

The clinical experiment indicates that the components of rotational and linear movements of

different subjects’ tremors were different.

According to the results of clinical measurements, the dominant frequency and modified

mean range of hand grasps correlated well with the UPDRS ratings. Their correlation coeffi-

cients were 0.94 and 0.86 respectively.

As quantitative rigidity assessment in PD is difficult and depends on many factors, a compari-

son experiment was carried out. Elastic and viscous values will be obtained through a least

squares estimation with all the data. Nine healthy subjects were tested with this system in two

experimental conditions: 1) normal state (relaxed); and 2) imitated rigidity state. In addition,

the subjects performed the assessment task with different frequencies and elbow movement

ranges. The result indicates that viscosity and elasticity in the imitate rigidity condition are

bigger than the normal condition (relaxed state). The effect sizes (Cohen's d) of the viscosity

and elasticity between normal state and imitated state are 1.61 and 1.36 respectively, which

means the difference is significant. Thus, this system can detect the ON-OFF fluctuations of

parkinsonian rigidity. Both the wrist movement angle and frequency have a small effect on

the viscosity, but have an elevated effect on the elasticity.

The future research consists of carrying out measurements with more PD patients. With the

measurement data, the correlation between mechanical impedance (viscosity, elasticity, and

movement frequency) and UPDRS scores can be determined. After that, the system can be

used for rigidity assessment in PD.

Nevertheless, attention must be paid to the signal processing methods:

Influence of noise in tremor assessment when the PD patient has a slight tremor.

Inconsistent movement and its influence in tremor amplitude during the tremor as-

sessment task, especially for peak power calculation with the PSD estimation.

Combination of the six-axis signals in both a gyroscope and an accelerometer, which

represent the rotational and the linear movement respectively.

Correlation between different sensor signals and the UPDRS ratings.

Repeatability of parameters with the same patient at different times.

Accurate quantification action tremor.

Summary and Outlook

106

8. Conclusions and Outlook

8.1 Conclusions

A portable monitoring system used to quantify all primary neurological symptoms in the PD

and ET during the DBS surgery is presented in this thesis.

According to the requirements of neurosurgeons, the concept of a glove monitoring system

used in DBS surgery is presented. This system is focused on the symptom assessments of PD

and ET.

According to the definitions of UPDRS and TETRAS scores and the situation during DBS

surgery, several of such tasks were chosen to detect the stimulating effects of the electrical

stimulation. Each assessment lasts for ten seconds. Passive flexion and extension of the elbow

is used to assess rigidity in this study. Tremor, bradykinesia, and rigidity assessments are per-

formed in a system based on IMUs and FSRs. An IMU can be used to measure both the rota-

tional and linear movement, using the combination of a triple-axis accelerometer and a triple-

axis gyroscope. This system consists of a glove part and a computer part. The communication

between these two parts is based on a USB cable. The measured parameters in different elec-

trode placements and stimulating intensities are listed in the GUI for comparison.

The positions of sensors are the wrist and fingers. All the sensors have no contact with the

human body.

The first version of the glove monitoring system based on the above concept includes two

portable prototypes: one for tremor/bradykinesia assessment and the other for rigidity assess-

ment. A wearable textile glove, two inertial measurement units (IMUs), two force sensor box-

es, a computer, and other components were included in the prototypes. The prototype of the

tremor/bradykinesia assessment system was based on an IMU attached to a finger. The first

prototype of the rigidity assessment system was based on two force sensor boxes and an IMU

on the wrist.

The verification of the analytical methods of the two prototypes was performed, compared

with an EM system. Because the quantitative rigidity assessment of PD patients is difficult

and depends on many factors, a comparison experiment was carried out. An examiner flexed

and extended the elbow joint of nine volunteers through a rigidity assessment cuff attached

around the wrist, both with the relaxed state and imitated rigidity state. The result indicates

that viscosity and elasticity in the imitated rigidity condition were bigger than they were in

normal conditions (relaxed state).

This system was tested in the hospital but outside the operating room first. Five patients were

tested with the tremor/bradykinesia monitoring system. The tremor measurement results indi-

cated that the tremor amplitude calculation with the PSD method was valid only in the con-

stant state, which was regarded as a valid state. For the action tremor amplitude calculation,

the quadratic mean method or other algorithms should be employed. The dominant tremor

frequency of a patient for one type of tremor task included small fluctuations even in an inva-

lid tremor state. The bradykinesia measurement results indicate that the dominant frequency

and modified mean range of hand grasps correlate well with the severity of bradykinesia.

Summary and Outlook

107

As a result, the prototype for tremor and bradykinesia assessment can quantify tremor and

bradykinesia. However, few patients were measured in this study. More measurements of pa-

tients with PD and ET are needed.

The prototype for rigidity assessment can detect the ON-OFF fluctuations of parkinsonian

rigidity. With further measurement data, the correlation between the UPDRS score and me-

chanical impedance (viscosity, elasticity, and movement frequency) and UPDRS score can be

determined.

Based on the disadvantages of the first version of the glove monitoring system and the sug-

gestions from the surgeons, a combined prototype for the assessment of all three primary

symptoms was also presented.

8.2 Outlook

In this thesis, the glove monitoring system in the DBS application is present. It is the world’s

first objective assessment system for the quantification of three primary symptoms of PD and

ET.

However, there are still some approaches to improve the system in the next step:

1) Further clinical measurements should be performed to modify the regression coeffi-

cients in the algorithms with the combined prototype.

2) Further signal processing algorithms need to be developed based on the hardware and

the measurement data. Automatic calibration algorithms during symptom assessments

can be adopted in addition to the initial calibration procedures.

3) The difference between parkinsonian tremor and ET needs to be further investigated.

4) The analysis as to whether there are single or multiple dominant frequencies during

tremor tasks also needs to be performed (Post et al., 2005).

5) The multi-symptom phenomenon in a single assessment task should be investigated.

6) Nine-axis sensor fusion algorithms and other statistical methods can be incorporated in-

to future designs, especially for the tremor quantification, to get better performance.

Although this system is supposed to be used during DBS surgery, there is no clinical meas-

urement in the operating room yet. Thus, there is still much work to be done in the near fu-

ture:

1) Dyskinesia assessment is important to detect the side effects of DBS surgery. It should

be integrated into the system in the next version.

2) For the comparison of slight symptoms during DBS surgery, much work should be

done. Because the symptoms fluctuate all the time, it is important to measure the stable

parameters.

3) A series of validations and modifications of this system should be carried out according

to the requirements of DBS surgery.

Summary and Outlook

108

4) This system can be combined with the MRI, MER or iMRI systems for the application

of DBS surgery. The effective combination of these different systems will result in a

more complete and accurate severity quantification of the symptoms of PD and ET.

5) Clinical experiments should be performed during DBS surgery.

Only after further experiments and compulsory validations both outside and inside the operat-

ing room is this system supposed to help select the optimal target location and the settings of

the stimulation parameters of the DBS electrodes during and after DBS surgery.

In addition, a glove monitoring system with a wireless communication interface can be im-

plemented for the extended applications, for example, the assessments of symptoms before

and after DBS surgery or at the patients’ home.

Glossary

109

9. Glossary

ADC An analog-to-digital converter converts a continuous voltage to

a digital value that represents the voltage.

AHRS An attitude heading reference system, which are either solid-

state or MEMS gyroscopes, accelerometers, and magnetometers

on all three axes, provide heading, attitude, and yaw infor-

mation. A form of nonlinear estimation, such as a Kalman filter,

is typically used to compute the solution from these multiple

sources.

CE Consumer electronics are electronic devices intended for every-

day use. Apple Inc. has invented the consumerization of infor-

mation technology in the 2010s.

CNS The central nervous system, which consists of the brain and

the spinal cord, contains the majority of the nervous system.

DBS Deep-brain stimulation is a surgical procedure used for some

types of disabling neurological symptoms, which includes PD,

ET, dystonia, and chronic pain.

DCM A direction-cosine-matrix (rotation matrix) is a 3x3matrix con-

taining the cosines of the rotation (each of the 9 possible pairs of

axes) of one frame relative to another.

DOF The degree of freedom of a mechanical system is the number of

parameters that may vary independently.

EM The electromagnetic tracking system is a spatial measurement

system which determines the location and orientation of an ob-

ject that is attached to sensor coils.

ET Essential tremor is a common progressive neurological move-

ment disorder and appears in the arms or hands only in the state

of movement such as eating or writing.

FSR Force sensing resistor is a passive component whose resistance

changes when a force or pressure is applied.

GPi Globus pallidus or paleostriatum is a sub-cortical structure of

the brain which performs the regulation of voluntary movement.

GUI The graphical user interface represents the information and in-

teractions available to a user through graphical icons and visual

indicators and as opposed to text-based interfaces.

Glossary

110

iMRI Interventional MRI is a real-time MRI which can be used in

surgery conditions. Asleep iMRI-guided DBS implantation has

many advantages. The electrodes are positioned with the patient

asleep in an MRI scanner instead of awake in the operating

room.

IMU Inertial measurement unit is a single unit that measures a sub-

ject’s velocity, orientation, and gravitational forces in three di-

mensions, using a combination of accelerometers and gyro-

scopes. It is the main component of an inertial navigation sys-

tem.

LID Levodopa-induced dyskinesia is a form of dyskinesia associated

with anti-parkinsonian medications (Levodopa). It often in-

volves the presence of involuntary movement (chorea, dystonia,

and athetosis) and diminished voluntary movements. DBS can

reduce LID or reduce L-DOPA dosage.

MBRS The Modified Bradykinesia Rating Scale rates the bradykinesia

expressions of speed, amplitude, and rhythm separately, while

the UPDRS rates bradykinesia as a whole. Thus more infor-

mation is provided for different PD patients.

MCU A microcontroller (µC, uC or MCU) is a small computer on a

single chip containing a processor core, memory, an I/O control

unit, and a clock.

MEMS Microelectromechanical systems or micro systems technology is

a technology that can be defined as miniaturized mechanical and

electro-mechanical elements (i.e., devices and structures) of

small scale that are made using the techniques of

microfabrication. Their general components are a microproces-

sor and several components that interact with the surroundings

such as microsensors.

MER Microelectrode recording is a neurosurgical technique of single

neuron activity. MER is realized with the patient awake and

interacting by performing a series of motor tasks so that the neu-

rophysiological mapping of the target is enhanced by functional

mapping to localize the optimal target of the DBS electrode.

MF Motor fluctuations (or ON-OFF fluctuations) refer to the state

changes between symptoms controlled (ON state) and symp-

toms not controlled (OFF state, symptoms are poor or not re-

sponding to the medication).

MRI Magnetic resonance imaging is a radiology medical imag-

ing technique used to visualize body structures in detail.

PD Parkinson’s disease is a central nervous system disease that

leads to movement disorders such as tremor, stiffness, and diffi-

Glossary

111

culty with walking and balance. It appears mainly in people

more than 60 years old, and the risk of developing PD goes up

with age.

PSD The power spectral density describes how the power estimation

of a signal or time series is distributed on the frequency domain.

The PSD is the Fourier transform of the autocorrelation function

of the signal which is a wide-sense stationary random process. If

the signal is not stationary, then its autocorrelation function is a

function of two variables. A similar technique, instead of PSD,

may be used to estimate a time-varying spectral density.

QFN The Quad Flat No-Lead is a surface mount plastic package with

leads located on four sides of the bottom package.

RFI The radio frequency interference refers to the information being

transmitted across unshielded copper cable, which can be inter-

fered with by the noise caused by other radio frequencies.

RMS The root mean square (quadratic mean) is a statistical measure

of the magnitude (the average of the squares) of a randomly

varying quantity.

STN The subthalamic nucleus is a small lens-shaped nucleus in the

brain, a component of the basal ganglia control system, and is

involved in action selection.

TETRAS The Essential Tremor Rating Assessment Scale consists of 10

items in which action tremor is rated 0–4 in 0.5 intervals. It ex-

cellently rates ET in term of estimated tremor amplitude.

TWI The two-wire interface protocol, which is the same as the IIC

protocol, uses two wires for serial data transmission between

two or more chips in asynchronous mode.

UPDRS The Unified Parkinson's Disease Rating Scale is a clinical rating

scale used to evaluate the symptom severities of PD by inter-

view and clinical observation. Other rating scales for PD, such

as the Hoehn and Yahr scale and the Schwab and England Ac-

tivities of Daily Living Scale, are included in the revised

UPDRS.

USB The universal serial bus (USB) is an industry standard that de-

fines the cables, connectors, and communications protocols used

in a 4-pin interface for connection, communication, and power

supply between a computer and another electronic device.

Bibliography

112

10. Bibliography

Abramowitz, M & Stegun, I.A. (1964): Handbook of mathematical functions with formulas,

graphs, and mathematical tables, New York: Dover.

Allen, D.P.; Playfer, J.R.; Aly, N.M.; Duffey, P.; Heald, A.; Smith, S.L. & Halliday D.M.

(2007): “On the use of low-cost computer peripherals for the assessment of motor dysfunction

in Parkinson's disease--quantification of bradykinesia using target tracking tasks”, IEEE

Trans. Neural. Syst. Rehabil. Eng., vol. 15, no. 2, pp. 286–294.

Bamberg, S.; Benbasat, A.Y.; Scarborough, D.M.; Krebs, D.E. & Paradiso, J.A. (2008): “Gait

analysis using a shoe-integrated wireless sensor system”, IEEE Trans. Inf. Technol. Biomed.,

vol. 12, no. 4, pp. 413–423.

Bittar, R.G.; Burn S. C.; Bain, P.G.; Owen, S.L.; Joint, C.; Shlugman, D. & Aziz, T.Z. (2005):

“Deep brain stimulation for movement disorders and pain”, Journal of clinical Neuroscience,

vol. 12, no. 4, pp. 457–463.

Bland, J.M. & Altman, D.G. (2003): “Applying the right statistics: analyses of measurement

studies”, Ultrasound Obstet. Gynecol., vol. 22, pp. 85–93.

Bour, L.J.; Contarino, M.F.; Foncke, E.M.; de Bie R.M.; van den Munckhof, P.; Speelman,

J.D. & Schuurman, P.R. (2010): “Long-term experience with intraoperative microrecording

during DBS neurosurgery in STN and GPi.”, Acta Neurochir., vol. 152, pp. 2069–2077.

Burkhard, P.R.; Langston, J.W. & Tetrud, J.W. (2002): “Voluntarily simulated tremor in

normal subjects”, Neurophysiol. Clin., vol. 32, no. 2, pp. 119–126.

Burkhard, P.R.; Shale, H.; Langstom, J.W. & Tetrud, J.W. (1999): “Quantification of dyski-

nesia in Parkinson’s disease: validation of a novel instrumental method”, Mov. Disord., vol.

14, no. 5, pp. 26–27.

Carmeli, E.; Vatine, J.J.; Peleg, S.; Bartur, G. & Elbo, E. (2009): “Upper limb rehabilitation

using augmented feedback: Impairment focused augmented feedback with HandTutor”, Vir-

tual Rehabilitation International Conference, pp. 220.

Charles S.M. (2003): “Parkinson’s Disease: Overview and Current Abstracts”, New York:

Nova Science Pub Inc., pp. 7–9.

Chaudhuri, K.R. & Ondo, W.F. (2011): “Handbook of Movement Disorders”, London:

Springer Healthcare Ltd, pp. 1–2.

Chwaleba, A.; Jakubowski, J. & Kwiatos, K. (2003): “The measuring set and signal pro-

cessing method for the characterization of human hand tremor”, International Conference on

CAD Systems in Microelectronics (CADSM), pp. 149–154.

Crawford, P. & Zimmerman, E.E. (2011): “Differentiation and diagnosis of tremor”, Ameri-

can Family Physician, vol. 83, no. 6, pp. 697–702.

Bibliography

113

Czabke, A.; D’Angelo, L.T.; Niazmand, K.; & Lueth, T.C. (2009): “ Ein kompaktes System

zur Erfassung und Dokumentation von Bewegungsgewohnheiten”, Conference on Ambinent

Assisted Living -AAL, Berlin, pp. 1–5.

Dai, H.; Otten, B.; Mehrkens, J.H.; D’Angelo, L.T. & Lueth, T.C. (2013a): “A novel glove

monitoring system used to quantify neurological symptoms during deep-brain stimulation

surgery”, IEEE Sensors Journal, vol. 13, no. 9, pp. 3193–3202.

Dai, H.; Otten, B.; Mehrkens, J.H. & D’Angelo, L.T. (2013b): “A portable system for quanti-

tative assessment of parkinsonian rigidity”, IEEE EMBC Conf., pp. 6591–6594.

D’Angelo, L.T.; Czabke, A.; Niazmand, K. & Lueth, T.C. (2010): “ART–a new concept for

an activity recorder and transceiver”, IEEE EMBS Conf., Buenos Aires, pp. 2132–2135.

D’Angelo, L.T.; Michael, S.; Paul, N. & Lueth, T.C. (2011): “A sensor network to iPhone

interface separating continuous and sporadic processes in Mobile Telemedicine”, IEEE EMBS

Conf., Boston, pp. 1528–1531.

Deuschl, G.; Bain, P. & Brin, M. (1998): “Consensus statement of the movement disorder

society on tremor”, Movement Disorders, vol. 13, pp. 2–23.

Dipietro, L., Sabatini, A.M. & Dario, P. (2008): “A survey of glove-based systems and their

applications”, IEEE Transactions on Systems, Man and Cybernetics, vol. 4, pp. 461–482.

Edwan, E.; Zhang, J.Y.; Zhou, J.C. & Loffeld, O. (2011): “Reduced DCM based attitude es-

timation using low-cost IMU and magnetometer triad”, 8th

Workshop on Positioning Naviga-

tion and Communication (WPNC), pp. 1–6.

Elble, R.J.; Pullman, S.L.; Matsumoto, J.Y.; Raethjen, J.; Deuschl, G.; Tintner, R. & Tremor

Research Group (2006): “Tremor amplitude is logarithmically related to 4- and 5-point tremor

rating scales”, Brain, vol. 129, no. 10, pp. 2660–2666.

Elble, R.J. & Deuschl, G. (2011): “Milestones in tremor research”, Movement Disorders, vol.

26, no. 6, pp. 1096–1105.

Ellermeier, W. & Faulhammer, G. (2000): “Empirical evaluation of axioms fundamental to

Stevens’s ratio-scaling approach: I. Loudness production”, Perception & Psychophysics, vol.

62, no. 8, pp. 1505–1511.

Espay, A.J.; Giuffrida, J.P.; Chen, R.; Payne, M.; Mazzella, F.; Dunn, E. et al. (2011): “Dif-

ferential response of speed, amplitude, and rhythm to dopaminergic medications in Parkin-

son’s disease”, Movement Disorders, vol. 26, no. 14, pp. 2504–2508.

Endo, R.; Yokoe, M.; Fukawa, K.; Sakoda, S. & Akazawa, K. (2007): “Measurement system

of finger-tapping contact force for quantitative diagnosis of Parkinson’s disease”, IEEE EMBS

Conf., pp. 1354–1357.

Endo, T.; Okuno, R.; Yokoe, M.; Akazawa, K. & Sakoda, S. (2009): “A Novel method for

systematic analysis of rigidity in Parkinson’s disease”, Movement Disorders, vol. 24,

pp. 2218–2224.

Gowers, W.R. (1886): “A Manual of Diseases of the Nervous System”, London: J. & A.

Churchill.

Bibliography

114

Giovannoni, G.; Schalkwyk, J.; Fritz, V.U. & Lees, A.J. (1999): “Bradykinesia akinesia inco-

ordination test (BRAIN TEST): an objective computerized assessment of upper limb motor

function”, Journal of Neurol Neurosurg Psychiatry, vol. 67, pp. 624–629.

Giuffrida, J.P.; Riley, D.E.; Maddux, B.N. & Heldman, D.A. (2009): “Clinically deployable

Kinesia technology for automated tremor assessment”, Movement Disorders, vol. 24, pp.

723–730.

Hauser, R.A.; Friedlander, J.; Zesiewicz, T.A.; Adler, C.H.;Seeberger, C.H.; O’Brien C.F.; et

al. (2000): “A home diary to assess functional status in patients with Parkinson’s disease with

motor fluctuations and dyskinesia”, Clinical Neuropharmacology, vol. 23, no. 2, pp. 75–81.

Heldman, D.A.; Giuffrida, J.P.; Chen, R.; Payne, M.; Mazzella, F.; Dunn, E. et al. (2011a):

“The modified bradykinesia rating scale for Parkinson’s disease: reliability and comparison

with kinematic measures”, Movement Disorders, vol. 26, pp. 1859–1863.

Heldman, D.A.; Jankovic, J.; Vaillancourt, D.E.; Prodoehl, J.; R.J. Elble & Giuffrida, J.P.

(2011b): “Essential tremor quantification during activities of daily living”, Parkinsonism &

Related Disorders, vol. 17, no. 7, pp. 537–542.

Hess, C.W. & Pullman, S.L. (2012): “Tremor: clinical phenomenology and assessment tech-

niques”, Tremor and Other Hyperkinetic Movements, vol. 2, pp. 1–15.

Hurtado, J.M.; Charles, C.M.; Tamas, L.B. & Sigvardt, K.A. (1999): “Dynamics of tremor-

related oscillations in the human globus pallidus: A single case study”, Proc. Natl. Acad. Sci.

U.S.A, vol. 96, no. 4, pp. 1674–1679.

Iacono, R.P.; Lonser, R.R.; Maeda, G.; Kuniyoshi, S.; Warner, D.; Mandybur, G, & Yamada

S. (1995): “Chronic anterior pallidal stimulation for Parkinson’s disease”, Acta Neurochir.,

vol. 137, pp. 106–112.

Iba´nez, J.; Serrano, J.I.; del Castillo, M.D. & Barrios, L.J. (2010): “An asynchronous BMI

system for online single-Trial detection of movement intention”, IEEE EMBS Conf., pp.

4562–4565.

Jankovic, J. (2008): “Parkinson’s disease: clinical features and diagnosis”, J. Neurol.

Neurosurg Psychiatry, vol. 79, no. 79, pp. 368–376.

Kazi, S.; Azizan, A.; Zain, Z.M.D. & Mailah, M. (2010): “Performance evaluation of smart

glove applied to experimental rig to control human hand tremor for Parkinson disease”, ACE

Proceedings of the 9th WSEAS international conference on Applications of computer engi-

neering, pp. 306–315.

Keijsers, N.L.; Horstink, M.W.; van Hilten, J.J.; Hoff, J.I. & Gielen, C.C. (2000): “Detection

and assessment of the severity of Levodopa-induced dyskinesia in patients with Parkinson’s

disease by neural networks”, Movement Disorders, vol. 15, no. 6, pp. 1104–1111.

Kim, J.W.; Lee, J.H.; Kwon, Y.; Kim, C.S.; Eom, G.M.; Koh, S.B.; Kwon, D.Y. & Park,

K.W. (2011a): “Quantification of bradykinesia during clinical finger taps using a gyroscope

in patients with Parkinson’s disease”, Medical Biology Engineering Computation, vol. 49, pp.

365–371.

Bibliography

115

Kim, J.W.; Kwon, J.; Kim, Y.M.; Chung, H.Y.; Eom, G.M.; Jun, J.H.; et al. (2011b): “Analy-

sis of lower limb bradykinesia in Parkinson's disease patients”, Geriatrics & Gerontology

International, vol. 12, no. 2, pp. 257–264.

Kringelbach, M.L.; Jenkinson, N.; Owen, S.L.F. & Aziz, T.Z. (2007): “Translational principle

of deep brain stimulation”, Nature Reviews Neuroscience, vol. 8, no. 8, pp. 623–635.

Levin, J.; krafczyk, S.; Valkovic, P.; Eggert, T.; Claasssen, J. & Bötzl, K. (2009): “Objective

measurement of muscle rigidity in Parkinsonian patients treated with subthalamic stimula-

tion”, Movement Disorders, vol. 24, no. 1, pp. 57–63.

LeMoyne, R.; Mastronianni, T.; Cozza, M.; Coroian, C. & Grundfest, W. (2010): “Implemen-

tation of an iPhone for characterizing Parkinson’s disease tremor through a wireless accel-

erometer application”, IEEE EMBS Conf., pp. 4954–4958.

LeMoyne, R.; Coroian, C. & Mastronianni, T. (2009): “Quantification of Parkinson’s disease

characteristics using wireless accelerometers”, IEEE Complex Medical Engineering Conf.

(CME/ICME), pp. 1–5.

Lötters, J.C.; Schipper, J.; Veltink, P.H.; Olthuis, W. & Bergveld, P. (1998): “Procedure for

in-use calibration of triaxial accelerometers in medical applications”, Sensors and Actuators

A: Physical, vol. 68, pp. 221–228.

Luce, R.D. & Krumhansl, C. (1988): “Measurement, scaling, and psychophysics”, in Stevens'

Handbook of Experimental Psychology (Atkinson R.C., Herrnstein R.J., Lindzey G., & Luce

R.D.), New York: Wiley, pp. 1–74.

Lueth, T.C.; D’Angelo, L.T. & Czabke, A. (2010): “Chart 4: TUM–AgeTech: A New

Framework for Pervasive Medical Devices”, in Pervasive and Smart Technologies for

Healthcare: Ubiquitous Methodologies and Tools (Coronato, A. & Pietro, G.D.). New York:

IGI Global, pp. 295–321.

Luinge, H.J. & Veltink, P. H. (2005): “Measuring orientation of human body segments using

miniature gyroscopes and accelerometers”, Med. Biol. Eng. Comput., vol. 43, pp. 273–282.

Louis, E.D. & Ferreira, J.J. (2010): “How common is the most common adult movement dis-

order? Update on the worldwide prevalence of essential tremor”, Mov. Disord., vol. 25, pp.

534–541.

Machado, A.; Rezai, A.R.; Kopell, B.H.; et al. (2006): “Deep brain stimulation for Parkin-

son’s disease: surgical technique and perioperative management”, Movement Disorders, vol.

21, Suppl. 14, pp. 247–258.

Madgwick, S.O.H.; Harrison, A.J.L. & Vaidyanathan, A. (2011): “Estimation of IMU and

MARG orientation using a gradient descent algorithm”, IEEE ICORR Conf., Zurich, pp. 1–7.

Mauro, R. (1990): “Understanding L.O:V.E. (Left Out Variables Error): a method for estimat-

ing the effects of omitted variables”, Psychological Bulletin, vol. 108, no. 2, pp. 314–329.

McClelland III, S. (2011): “A cost analysis of intraoperative microelectrode recording during

subthalamic stimulation for Parkinson’s disease”, Movement Disorders, vol. 26, no. 8, pp.

1422–1427.

Bibliography

116

Mera, T.O.; Vitek, J.L.; Alberts, J.L. & Giuffrida, J.P. (2011): “Kinematic optimization of

deep brain stimulation across multiple motor symptoms in Parkinson’s disease”, J. Neurosci.

Methods, vol. 198, no. 2, pp. 280–286.

Mera, T.O.; Burack, M.A. & Giuffrida, J.P. (2012): “Quantitative assessment of Levodopa-

induced dyskinesia using automated motion sensing technology”, IEEE EMBS Conf., pp.

154–157.

Moore, G.P.; Ding, L. & Bronte-Stewart, H.M. (2000): “Concurrent Parkinson tremors”, J

Physiol., vol. 529, pp. 273–281.

Mostile, G.; Giuffrida, J.P.; Adam, O.R.; Davidson, A. & Jankovic, J. (2010): “Correlation

between Kinesia system assessments and clinical tremor scores in patients with essential

tremor movement”, Movement Disorders, vol. 25, no. 12, pp. 1938–1943.

Nakagawa, S. & Cuthill, I.C. (2010): “Effect size, confidence interval and statistical signifi-

cance: a practical guide for biologists”, Biological Reviews Cambridge Philosophical Society,

vol. 82, pp. 591–605.

Narcisa, V.; Aguilar, D.; Nquyen, D.V.; et al. (2011): “A quantitative assessment of tremor

and ataxia in female FMR1 premutation carriers using CATSYS”, Curr. Gerontol. Geriatr.

Res., vol. 2011, pp. 1–7.

National Instrument Inc. (2011): LabVIEW User Tutorials. USA.

NDI Digital Inc. (2010): Aurora V2 User Guide. Canada.

Niazmand, K.; Jehle, C.; D’Angelo, L.T. & Lueth, T.C. (2010): “A new washable low-cost

garment for everyday fall detection”, IEEE EMBS Conf., Buenos Aires, pp. 6377–6380.

Niazmand, K.; Tonn, K.; Kalaras, A.; Fietzek, U.M.; Mehrkens, J.H. & Lueth, T.C. (2011a): “

Quantitative evaluation of Parkinson's disease using sensor based smart glove”, IEEE Sympo-

sium on Computer-Based Medical Systems-CBMS, Bristol, pp. 1–8.

Niazmand, K., Kalaras, A., Dai, H., Lueth, T.C. (2011b): “Comparison of methods for tremor

frequency analysis for patients with Parkinson’s disease”, The 4th International Conference

on BioMedical Engineering and Informatics (IEEE CBEI), Shanghai, pp: 1312-1316.

Norman, K.E.; Edwards, R. & Beuter, A. (1999): “The measurement of tremor using a veloci-

ty transducer: comparison to simultaneous recordings using transducers of displacement, ac-

celeration and muscle activity”, Journal of Neuroscience Methods, vol. 92, pp. 41–54.

Okun, M.S. & Foote, K.D. (2010): “Parkinson’s disease DBS: what, when, who and why?

The time has come to tailor DBS targets”, Expert Review Neurotherapeutics, vol. 10, pp.

1847–1857.

Okuno, R.; Yokoe, M.; Akazawa, K.; Abe, K. & Sakoda, S. (2006): “Finger taps movement

acceleration measurement system for quantitative diagnosis of Parkinson’s disease”, IEEE

EMBS Conf., Suppl, pp. 6623–6626.

Olivares, A.; Olivares, G.; Gorriz, J.M. & Ramirez, J. (2009): “High-efficiency low-cost ac-

celerometer-aided gyroscope calibration”, IEEE International Conference on Test and Meas-

urement (ICTM), pp. 354–360.

Bibliography

117

Oluigbo, C.O.; Salma, A. & Rezai, A.R. (2012): “Deep brain stimulation for neurological

disorders”, IEEE Reviews in Biomedical Engineering, vol. 5, pp. 88–99.

Ostrem, J.L.; Galifianakis, N.B.; Markun, L.C.; Grace, J.K.; Martin, A.J.; Starr, P.A. & Lar-

son PS. (2013): “Clinical outcomes of PD patients having bilateral STN DBS using high-field

interventional MR-imaging for lead placement”, Clin. Neurol. Neurosurg., vol. 115, no. 6, pp.

708–712.

O'Suilleabhain, P.E. & Matsumoto, J.Y. (1998): “Time-frequency analysis of tremors”, Brain,

vol. 121, pp. 2127–2134.

Park, B.K.; Kwon, Y.; Kim, J.W.; Lee, J.H.; Eom, G.M.; Koh, S.B. et al. (2011): “Analysis of

viscoelastic properties of wrist joint for quantification of parkinsonian rigidity”, IEEE Trans-

actions on Neural Systems and Rehabilitation Engineering, vol. 19, no. 2, pp. 167–176.

Patel, S.; Hughes, R.; Huggins, N.; Standaert, D.; Growdon, J.; Dy, J. & Bonato, P. (2008):

“Using wearable sensors to predict the severity of symptoms and motor complications in late

stage Parkinson's disease”, IEEE EMBS Conf., pp. 3686–3689.

Patel, S.; Lorincz, K.; Hughes, R.; Huggins, N.; Growdon, J.; Standaert, D. et al. (2009):

“Monitoring motor fluctuations in patients with Parkinson’s disease using wearable sensors”,

IEEE Trans. Inf. Technol. in Biomed., vol. 13, no. 6, pp. 864–873.

Patrick, S.K.; Denington, A.A.; Gauthier, M.J.; Gillard, D.M. & Prochazka, A. (2001):

“Quantification of the UPDRS rigidity scale”, IEEE Trans. Neural Syst. Rehabil. Eng., vol. 9,

pp. 31–41.

Pahwa, R. & Lyons, K.E. (2007): Handbook of Parkinson’s Disease (4th ed.), New York:

Informa Healthcare (USA) Inc.

Pavel, P. & Popelka, J. (2012): “IMU aiding using two AHRS units”, IEEE/AIAA Digital Avi-

onics System Conference (DASC), pp. 5B1-1–5B1-13.

Post, B.; Maruschka, M.P.; de Bie, R.M.; de Haan, R.J. & Speelman, J.D. (2005): “Unified

Parkinson's disease rating scale motor examination: are ratings of nurses, residents in neurol-

ogy, and movement disorders specialists interchangeable?”, European Journal of Nuclear

Medicine and Molecular Imaging, vol. 20, no. 12, pp. 1577–1584.

Powell, Jr.H.C.; Hanson, M.A. & Lach, J. (2007): “A wearable inertial sensing technology for

clinical assessment of tremor”, IEEE Biomedical Circuits and Systems Conference, BIOCAS

2007, pp. 9–12.

Popovic, L.Z., Sekara, T.B. & Popovic, M.B. (2008): “Adaptive band-pass filter (ABPF) for

tremor extraction from inertial sensor data”, Computer Methods and Programs in Biomedi-

cine, vol. 99, pp. 298–305.

Prochazka, A.; Bennett, D.J.; Stephens, M.J.; Patrick, S.K.; Sears-Duru, R.; Roberts, T. &

Jhamandas J.H. (1997): “Measurement of rigidity in Parkinson’s disease”, Movement Disor-

ders, vol. 12, pp. 24–32.

Bibliography

118

Richard, G.B.; Sasha, C.B.; Peter, G.B.; Sarah, L.O.; Carol, J.; David, S. & Tipu, Z.A. (2005):

“Deep brain stimulation for movement disorders and pain”, Journal of Clinical Neuroscience,

vol. 12, no. 4, pp. 457–463.

Rigas, G.; Tzallas, A.T.; Tsalikakis, D.G.; Konitsiotis, S. & Fotiadis, D.I. (2009) “Real-time

quantification of rest tremor in the Parkinson’s disease”, IEEE EMBS Conf., Minneapolis, pp.

1306-1309.

Rissanen, S.M.; Kankannpää, M.; Tarvainen, M.P.; Nuutinen, J.; Tarkka, I.M.; Airaksinen, O.

& Karialainen, P.A. (2007): “Analysis of surface EMG signal morphology in Parkinson’s

disease”, Physiological Measurement, vol. 28, pp. 1507–1521.

Rissanen, S.M.; Kankaanpaa, M.; Tarvainen, M. P.; Novak, V.; Hu, K.; Manor, B. et al.

(2011): “Analysis of EMG and acceleration signals for quantifying the effects of Deep Brain

Stimulation in Parkinson’s disease”, IEEE Trans. Biomed. Eng., vol. 99, pp. 1–9.

Riviere, C.N.; Reich, S.G. & Thankor, N.V. (1997): “Adaptive Fourier modelling for quanti-

fication of tremor”, Journal of Neuroscience Methods, vol. 74, pp: 77–87.

Sakoda, S.; Akazawa, K.; Okuno, R.; Yokoe, M. & Endo, T. (2011): “Muscle tonus measur-

ing apparatus”, United States Patent Application Publication, pub. no.: US2011/0087128 A1.

Salarian, A.; Russmann, H.; Wider, C.; Burkhard, P.R.; Vingerhoets, F.J. & Aminian, K.

(2007): “Quantification of tremor and bradykinesia in Parkonson’s disease using a novel am-

bulatory monitoring system”, IEEE Trans. Biomed Eng., vol. 54, pp. 313–322.

Sepehri, B.; Esteki, A.; Ebrahimi-Takamjani, E. et al. (2007): “Quantification of rigidity in

Parkinson’s disease”, Ann. Biomed. Eng., vol. 35, pp. 2196–2203.

Singh, A.; Kammermeier, S.; Mehrkens, J.H. & Bötzel, K. (2012): “Movement kinematic

after deep brain stimulation associated microlesions”, J. Neurol. Neurosurg. Psychiatry, vol.

82, pp. 1022–1026.

Shaeffer, D.K. (2013): “MEMS inertial sensors: A tutorial overview”, IEEE Communications

Magazine, vol. 51, no.4, pp. 100–109.

Shany, T.; Redmond, S.J.; Narayanan, M.R. & Lovell, N.H. (2012): “Sensors-based wearable

systems for monitoring of human movement and falls”, IEEE Sensors Journal, vol. 12, no. 3,

pp. 658–670.

Shapiro, M.B.; Vaillancourt, D.E.; Sturman, M.M.; et al. (2007): “Effects of STN DBS on

rigidity in Parkinson’s disease”, IEEE Trans. Neural. Syst. Rehabil. Eng., vol. 15, pp. 173–

181.

Shima, K.; Tsuji, T.; Kan, E.; Kandori, A., Yokoe, M. & Sakoda, S. (2008): “Measurement

and evaluation of finger tapping movements using magnetic sensors”, IEEE EMBS Conf., pp.

5628–5631.

Smeja, M.; Foerster, F.; Fuchs, G.; Emman, D.; Hornig, A. & Fahrenberg, J. (1999): “24-h

assessment of tremor activity and posture in Parkinson’s disease by multi-channel

acceelerometry”, Journal of Psychosiology, vol. 13, pp. 245–256.

Bibliography

119

Spieker, S.; Jentgens, C.; Boose, A. & Dichgans, J. (1995): “Reliability, specificity and sensi-

tivity of long-term tremor recordings”, Electroencephalography and Clinical Neurophysiolo-

gy, vol. 97, pp. 326–331.

Spyers-Ashby, J.M. & Stokes, M.J. (2000): “Reliability of tremor measurements using a mul-

tidimensional electromagnetic sensor system”, Clin. Rehabil., vol. 14, no. 4, pp. 425–432.

Sturman, M.M.; Vaillancourt, D.E.; Metman, L.V.; Bakay, R.A.E & Corcos, D.M. (2004):

“Effects of subthalamic nucleus stimulation and medication on rest and postural tremor in

Parkinson’s disease”, Brain, vol. 127, pp. 2131–2143.

Su, Y.; Allen, C.R.; Geng, D.; Burn, D.; Brechany, U.; Bel, G.D. & Rowland, R.(2003): “3-D

motion system ("data-gloves"): application for Parkinson's”, IEEE Trans. Instr. Measur., vol.

52, no. 3, pp. 662–674.

Synnott, J.; Chen, L.; Nugent, C.D. & Moore, G. (2012): “WiiPD-objective home assessment

of Parkinson’s disease using the Nintendo Wii remote”, IEEE Trans. Inf. Technol. in Biomed.,

vol. 16, no. 6, pp. 1304–1312.

Tagliati, M.; Guten, G.N. & Horne, J. (2007): “Parkinson’s Disease for Dummies”, Indianap-

olis: Wiley Publishing Inc.

Takuyuki, E.; Masaru, Y.; Harutoshi, F. & Saburo, S. (2011): “Chapter 9: Novel Methods to

Evaluate Symptoms in Parkinson’s Disease–Rigidity and Finger Tapping”, in Diagnostics and

Rehabilitation of Parkinson’s Disease (Dushanova, J.), Croatia: InTech, pp. 191–206.

Teräväinen, H. & Calne, D.B. (1980): “Action tremor in Parkinson's disease”, J. Neurol.

Neurosurg. Psychiatry, vol. 43, no. 3, pp. 257–267.

Timmer, J.; Gantert, C.; Deuschl G. & Honerkamp, J. (1993): “Characteristics of hand tremor

time series”, Biol. Cybern., vol. 70, pp. 75–80.

Timmer, J.; Lauk, M. & Lücking, C.H. (1997): “Confidence regions for spectral peak fre-

quencies”, Biometrical Journal, vol. 39, no. 7, pp. 849–861.

Timmer, J.; Lauk, M.; Vach, W. & Lücking, C.H. (1999): “A test for a difference between

spectral peak frequencies”, Journal Computational Statistics & Data Analysis, vol. 30, no. 1,

pp. 45–55.

Timmer, J.; Häussler, S.; Lauk, M. & Lücking, C.H. (2000a): “Pathological tremors: deter-

ministic chaos or nonlinear stochastic oscillators?”, Chaos, vol. 10, no. 1, pp. 278–288.

Timmer, J.; Lauk, M.; Häußler, S. & Radt, V. (2000b): “Cross-spectral analysis of tremor

time series”, International Journal of Bifurcation and Chaos, vol. 10, no. 11, pp. 2595–2610.

Thümler, R. (2002): “Morbus Parkinson: Ein Leitfaden für Klinik und Praxis”, Berlin:

Springer.

Van Dillen, L.R.; Roach, K.E. (1988): “Interrater Reliability of a Clinical Scale of Rigidity”,

Physical Therapy, vol. 68, pp. 1679–1681.

Bibliography

120

Van Someren, E.J.; Vonk, B.E.; Thijssen, W.A.; Speelman, J.D.; Schuurman, P.R.; Mirmiran,

M. & Swaab, D.F. (1998): “A new actigraph for long-term registration of the duration and

intensity of tremor and movement”, IEEE Trans. Biomed. Eng., vol. 45, no. 3, pp. 386–395.

Veluvolu K.C. & Ang, W.T. (2011): “Estimation of physiological tremor from accelerometers

for real-time applications”, Sensors, vol. 11, no. 3, pp. 3020–3036.

Winestone, J.S.; Zaidel, A.; Bergman, H. & Israel, Z. (2006): “The use of macroelectordes in

recording cellular spiking activity”, Clinical Neuroscience, vol. 206, pp. 34–39.

Winqeier, B.; Tcheng, T.; Koop, M.M.; Hill, B.C.; Heit, G. & Bronte-Stewart, H.M. (2006):

“Intra-operative STN DBS attenuates the prominent beta rhythm in the STN in Parkinson’s

disease”, Exp. Neurol., vol. 197, no. 1, pp. 244–251.

Yuting, Z.; Markovic, S; Wagenaar, R.C. & Little, T.D.C. (2011): “Continuous functional

activity monitoring based on wearable tri-axial accelerometer and gyroscope”, Pervasive

Computing Technologies for Healthcare (PervasiveHealth), Dublin, vol. 99, pp. 1–9.